Methods and systems of assertional simulation

ABSTRACT

Dereferencing comprises separating content of a source from structure of the source and separating content of the source from a meaning of the content within the structure.

CLAIM TO PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/841,990 filed Apr. 7, 2020. Ser. No. 16/841,990 is a continuation ofU.S. patent application Ser. No. 16/238,122 filed Jan. 2, 2019. Ser. No.16/238,122 issued as U.S. Pat. No. 10,650,313 on May 12, 2020. Ser. No.16/238,122 is a continuation in part of International Patent ApplicationSerial No. PCT/US17/40252 filed Jun. 30, 2017. The entirety of each ofthe preceding applications is incorporated herein.

BACKGROUND 1. Field

This disclosure relates to the fields of analytics and of modeling andsimulation and more particularly relates to a platform, with varioustechnology components, for improved handling of alternative points ofview of individuals involved in decision-making situations.

2. Description of the Related Art

Individuals in business situations and other situations frequently facedecisions that depend on the analysis of facts. Big data platforms,analytic tools, and similar systems allow individuals to apply variousanalytic techniques to data sets (such as financial and operational dataof an enterprise) to draw conclusions, support arguments, and the like.However, despite the fact that the structure or organization of data, ofan enterprise, or of its activities can strongly influenceunderstanding, such systems do not readily facilitate presentation of avariety of potential structures or organizations for these and otheritems, such as ones reflecting different points of view. Simulationsystems exist, but these also typically fail to readily enable handlingalternative points of view. A need exists for methods and systems forfacilitating modeling, simulation and other analytic activities thatfacilitate the improved handling of alternative points of view.

SUMMARY

In embodiments a system for decision support may include a referencedata set configured from a plurality of sources of data, the referencedata set either configured through or occurring as a result of forcedindependence of content and structure present in the sources of datasuch that at least one constraint, such as a semantic constraint upon anitem of content as a result of the structure present in a correspondingsource of data of the sources of data is not present in the referencedata set by being intentionally omitted during configuration of thereference data set. The at least one constraint may be a meaning of theitem of content configured in a schema for the corresponding source ofdata. In embodiments, the at least one constraint may be a forcedrelationship of the item of content with another item of content in theplurality of sources of data. In embodiments, the structure present maybe any of a relational database structure, a tabular structure, aformula, a hierarchy, and the like. In embodiments, the at least oneconstraint may be a semantic constraint that may be a definition of theitem of content or may be defined in an ontology for a correspondingsource of data in the plurality of sources of data. In embodiments, atleast one item of content from at least one of the sources of data maybe configured in the reference data set independent of at least onesemantic constraint present in the at least one of the sources of data.In embodiments, the semantic constraint may be imposed upon the at leastone item of content as a result of the structure the at least one of thesources of data. Further, in embodiments, at least one constraint uponan item of content due to the structure of a source of data of thesources of data comprising the item of content may be represented in thereference data set as an item of content. Also, at least one label of anitem of content reflective of a structure of a source of data of thesources of data comprising the item of content may be configured in thereference data set as an item of content. Yet further, configuration ofthe reference data set may include isolating content and structure ofthe sources of data and including the isolated content and datadescriptive of the structure as data elements in the reference data set.In embodiments, the system may further comprise an ontology for thereference data set that defines items of content in the reference dataset including items of content that represent structure in the sourcesof data.

In embodiments, a system for decision support may include methods thatmay include a method of de-referencing. The method may include taking asource of data that includes data elements defined by a source of dataontology and that are related by a structure of the source of data;extracting the data elements from the source of data independent oftheir ontology-specific definitions or structure-specific relationship;and configuring the extracted data elements in a reference data setdefined by a second ontology that is independent of the first ontologywhile ensuring data element-specific reference to the data elements inthe source of data.

In embodiments, a system for decision support may include methods thatmay include a method of de-referencing. The method may include stepsincluding determining at least one ontology-derived constraint on dataelements in a source of data; determining at least one structure-derivedconstraint on data elements in the source of data; and reproducing aportion of the data elements found in the source of data in a referencedata set while removing the ontology-derived and the structure-derivedconstraints. In embodiments, the at least one ontology-derivedconstraint comprises a meaning of the data elements. In embodiments, theat least one ontology-derived constraint comprises a context-specificconnection among the data elements. In embodiments, the at least onestructure-derived constraint comprises a relevance among the dataelements. In embodiments, the system may further comprise including datarepresentative of the at least one ontology-derived constraint as dataelements in the reference data set. In embodiments, the system mayfurther comprise including data representative of the at least onestructure-derived constraint as data elements in the reference data set.In embodiments, the reference data set may be configured as a flat datafile in which the data elements found in the source of the data and dataelements that represent at least one of the ontology-derived constraintand the structure-derived constraint appear in the reference data set.

In yet other embodiments, de-referencing may include accessing a datasource, the data source comprising content and structure organizedaccording to a first ontology, wherein the first ontology conveysmeaning to the content and wherein the structure facilitates detectingrelationships among individual items of the content; copying a portionof the content into a de-referenced data set that uses a second ontologyto organize the content, wherein a structure of the de-referenced dataset is independent of the structure of the data source; and generatingitems of content in the de-referenced data set that represent thestructure.

De-referencing may, in embodiments, include accessing a data setorganized to comply with a first ontology, wherein at least two elementsin the data set are related through a third element that is defined inthe first ontology, and generating a reference data model organized tocomply with a second ontology, wherein each of the at least two elementsand the third element are stored in the reference data model independentof relationships evident in the data set among the elements. Inembodiments, the third element may be any of a label for the at leasttwo elements and a data type. In embodiments, the at least two elementsare distinct data values of different data types. In embodiments,generating a reference data model may include severing at least one ofgeometric and textual relationships between the at least two elements.In embodiments, generating a reference data model may include severinginterconnections between the at least two elements, wherein theinterconnections may be defined in the first ontology, may includelinked lists, include tree structures, taxonomic structures and thelike. In embodiments, generating a reference data model may includecollapsing a taxonomic structure that relates the at least two elements.In embodiments, the taxonomic structure may be determined by the firstontology. In embodiments, a relationship between the at least twoelements may be based on the first ontology. In embodiments, arelationship between the at least two elements in the reference datamodel may be based on the second ontology. Additionally, generating areference data model may include executing mathematical operations thatmaintain a value of the at least two elements while removing structuralconfigurations in the data set associated with the at least twoelements.

In embodiments, a system for decision support may include a recombinantaccess and mediation system comprising a plurality of entity rootcertificates, each entity root certificate comprising datarepresentative of capabilities, preferences, and activity history of acorresponding entity. The recombinant access and mediation system mayfurther include an access facility that produces directed graphs ofentity root certificates of at least two entities. The recombinantaccess and mediation system may further include a mediation facilitythat perform functions including collocating the directed graphs of theplurality of access capabilities; determining access capabilities thatare common to the at least two entities based on intersections of thecollocated directed graphs; and generating an aggregated associationcertificate that indicates the common access capabilities, wherein theaggregated association certificate defines entity access rights of theat least two entities.

In embodiments, a system for decision support may include an accesssecurity system comprised of a plurality of authenticated entity rootcertificates, each root certificate representing at least one ofcapabilities, preferences, and activity history for a correspondingentity and an access rights determination facility, that producesdirected graphs of root certificate information for at least twoentities requesting access mediation, aligns the directed graphs so thatroot certificate information that is common for the at least twoentities is determined from intersections of the aligned graphs, andproduces an aggregated association certificate of common rootcertificate information that defines access rights of an aggregation ofthe at least two entities.

In embodiments, a distributed ledger access security system may includea plurality of authenticated access certificates for a plurality of peerentities seeking access to a distributed ledger, each root certificaterepresenting at least one of capabilities, preferences, and activityhistory for a corresponding entity and a distributed ledger accesssecurity facility. The distributed ledger access facility may convertcontent of the authenticated access certificates associated with one ofthe peer entities seeking access to the distributed ledger intoindividual graphs, aligns the individual peer entity graphs withcorresponding graphs of an authenticated access certificate of thedistributed ledger, and based on intersection of the peer entity graphswith the distributed ledger access graph determines rights of access bythe one of the peer entities to the distributed ledger. In embodiments,access to the distributed ledger is an access to update a block chainpresent in the distributed ledger.

In embodiments, a method of authenticating a transaction in a blockchain of transactions of digital resources, may include the stepscomprising generating directed graphs of data representative ofdistributed ledger access attributes of participants in a transactionrequest associated with digital resources tracked in the distributedledger, collocating the directed graphs of the participants, determiningat least one point of intersection of the directed graphs, whereinpoints of intersection represent access attributes that the participantshave in common, and based on the common access attributes, mediatingvalidation of the request by producing a validation certificate of thetransaction request. In embodiments, the validation certificateindicates at least one of that the transaction request is validated andthe transaction request is not validated. In embodiments, the validationcertificate indicates that points of intersection are insufficient tovalidate the transaction request. In embodiments, the participants inthe transaction request include a first user, the digital resources anda second user. In embodiments, the access attributes include at leastone of capabilities, preferences, and activity history of a participant.The access attributes may include a public key of the participant, aprivate key of the participant, a hash of a public key and a private keyof the participant, and the like. In embodiments, the transaction is anaccess to update a block chain present in the distributed ledger.

In embodiments, a system for decision support may include a system oftopical capabilities mediation that may include a Reference Data Modelcomprising a set of capabilities of an entity associated with a topicfor which the entity requests use of at least one capability of the setof capabilities in association with a node in a hierarchy of nodes; anAssertion Model with an associated Apportionment sub-Model which jointlycomprise a set of capability requirements for using a capability at eachnode in the hierarchy of nodes; and an Outcome Model which embodies aprojection of the capability requirements upon the set of capabilitiesof the entity for use thereof in association with a target node in thehierarchy of nodes. In embodiments, the Outcome Model further comprisesa sequence of projections of capability requirements of each higher nodein the hierarchy of nodes along a path in the hierarchy from a top nodeto the target node. In embodiments, each node in the hierarchy of nodesis a jurisdiction in a hierarchy of jurisdictions. In embodiments, thejurisdictions are legal jurisdictions of a country.

In embodiments, a system for decision support may include a method oftopical mediation within a hierarchy of nodes. The method may include astep of identifying a path through a hierarchy from a first node to atarget node in the hierarchy based upon a request for conductingtopic-specific activities in association with the target node. Themethod may include a step of projecting an Assertion Model comprising ahierarchical set of requirements for conducting topic-specific activityunder requirements for the first node upon elements of a Reference DataModel representing topic-specific capabilities of an entity therebyproducing a first Outcome Model representing first node allowedtopic-specific capabilities of the entity. The method may include a stepof projecting the Assertion Model for conducting the topic-specificactivity under requirements for the target node upon elements of thefirst Outcome Model thereby producing a second Outcome Modelrepresenting target node allowed topic-specific capabilities of theentity.

In embodiments, a system of Topical Capability Mediation (TCM) mayinclude a Reference Data Model comprising a set of capabilities of anentity associated with a topic for which the entity requests use of atleast one capability of the set of capabilities in a first jurisdiction.The system of Topical Capability Mediation (TCM) may also include anAssertion Model with an associated Apportionment sub-Model which jointlycomprise a hierarchical set of jurisdictional requirements associatedwith the topic, the set comprising topic use requirements for aplurality of jurisdictions. The system of Topical Capability Mediation(TCM) may include a first Outcome Model which embodies a projection ofjurisdictional requirements for a top-level jurisdiction in thehierarchical set jurisdictional requirements upon the set ofcapabilities of the entity for use thereof in the jurisdiction. Thesystem of Topical Capability Mediation (TCM) may include a plurality ofsecondary Outcome Models, each embodying a projection of jurisdictionalrequirements for a lower level jurisdiction in the hierarchical set ofjurisdictional requirements upon a set of capabilities of the entityembodied in an Outcome Model for a next higher level jurisdiction in thehierarchical set of jurisdictional requirements.

In embodiments, a system of topical mediation may include a ReferenceData Model comprising a set of capabilities of an entity associated witha topic for which the entity requests use of at least one capability ofthe set of capabilities in a jurisdiction. The system of TopicalCapability Mediation (TCM) may include an Assertion Model with anassociated Apportionment sub-Model which jointly comprise a set oftopic-specific requirements for use of the at least one capability in aplurality of jurisdictions arranged in a hierarchy, wherein thetopic-specific requirements for the jurisdiction is disposed at asub-level of the hierarchy; The system of Topical Capability Mediation(TCM) may also include a first Outcome Model which embodies a projectionof jurisdictional requirements for at least one jurisdiction at a levelin the hierarchy above the jurisdiction upon the set of capabilities ofthe entity for use thereof in the at least one jurisdiction and alljurisdictions at sub-levels below the at least one jurisdiction. Thesystem of Topical Capability Mediation (TCM) may also include a secondOutcome Model which embodies a projection of jurisdictional requirementsfor the jurisdiction at the sub-level of the hierarchy upon the set ofcapabilities of the entity embodied in the first Outcome Model.

In embodiments, a method of Topical Capability Mediation (TCM) within ahierarchy of nodes may include performing various steps including:identifying a path through a hierarchy from a first node to a targetnode in the hierarchy based upon a request for conducting topic-specificactivities in association with the target node; projecting an AssertionModel comprising a hierarchical set of requirements for conductingtopic-specific activity under requirements for the first node uponelements of a Reference Data Model representing topic-specificcapabilities of an entity thereby producing a first Outcome Modelrepresenting first node allowed topic-specific capabilities of theentity; and projecting the Assertion Model for conducting thetopic-specific activity under requirements for the target node uponelements of the first Outcome Model thereby producing a second OutcomeModel representing target node allowed topic-specific capabilities ofthe entity. In embodiments, the nodes in the hierarchy comprisejurisdictions and topic-specific activity comprises conductingtopic-specific business in a jurisdiction.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a global representation of the system components inthe Decision Time System (DTS) Platform;

FIG. 2 illustrates an assembly and an execution flow in the competitiveevaluation of Outcome Models from DTS Assertional Simulation;

FIG. 3 illustrates an assembly and an execution flow of components thatenhance Reference Data Models used in DTS Assertional Simulation withsupplementary information;

FIG. 4 illustrates an assembly and an execution flow that enhancesAssertion-Apportionment pairs used in DTS Assertional Simulation withsupplementary information deploying looped optimization;

FIG. 5 illustrates an assembly and an execution flow in the constructionand revision of Reference Data Models used in DTS Assertional Simulationfrom disparate information sources;

FIG. 6 illustrates an assembly and an execution flow in the constructionand revision of Assertion-Apportionment pairs used in DTS AssertionalSimulation using a looping optimization, competitive algorithm;

FIG. 7 illustrates an assembly and an execution flow in the constructionand revision of Reference Data Models and Assertion-Apportionment pairsfrom disparate information sources using semantic disconnectiontechniques to execute DTS Assertional Simulation;

FIG. 8 illustrates an assembly and an execution flow in thereconciliation of disparate information sources using semanticdisconnection techniques to execute DTS Assertional Simulation;

FIG. 9 illustrates an assembly and an execution flow, and an exampledata structure used in the construction and revision of Reference DataModels using semantic disconnection techniques;

FIG. 10 illustrates an assembly and an execution flow used in theconstruction and revision of Assertion-Apportionment pair usingcontent-emergent extraction techniques;

FIG. 11 illustrates the “single-Assertion-single-Reference” modality ofDTS Assertional Simulation;

FIG. 12 illustrates the “single-Assertion-multiple-Reference” modalityof DTS Assertional Simulation;

FIG. 13 illustrates the “multiple-Assertion-single-Reference” modalityof DTS Assertional Simulation;

FIG. 14 illustrates the “multiple-Assertion-multiple-Reference” modalityof DTS Assertional Simulation;

FIG. 15 illustrates certain entity types that may be involved in DTSAssertional Simulation;

FIG. 16 illustrates relationships between entity types andentity-associated auxiliary data in execution of DTS AssociationalProcess Management and DTS RAMS and DTS TCM;

FIG. 17 illustrates indexing entities with objects in execution of DTSAssociational Process Management;

FIG. 18 illustrates elements of Recombinant Access Mediation System(RAMS) execution sequence;

FIG. 19 illustrates elements of an event sequence involving a RAMS;

FIG. 20 illustrates an execution flow involving a RAMS events and RAMScertificate generation;

FIG. 21 illustrates relationships between entities and associatedancillary data used in of DTS Associational Process Management and DTSRAMS and DTS TCM;

FIG. 22 illustrates indexing of ancillary data to user objects andassociation of related policies to the data;

FIG. 23 illustrates indexing of ancillary data to processes andassociation of related policies to the data;

FIG. 24 illustrates association of internal and external system-enableddata elements and groups of elements;

FIG. 25 illustrates association of local access policies to various dataelements;

FIG. 26 illustrates an example among many possible examples of animplementation of entity grouping of user entity aggregation;

FIG. 27 illustrates elements of dynamic data aggregation and generationof certificates in a RAMS;

FIG. 28 illustrates aggregation of system processes and system-enabledprocesses;

FIG. 29 illustrates aggregation of data elements;

FIG. 30 illustrates aggregation of various entity types, includingusers, system processes and data elements;

FIGS. 31, 31A, and 31B illustrate a flow involving generation ofcertificates in connection with the RAMS system;

FIGS. 32 and 33 illustrate operation of a RAMS system upon one or moreobject-based, dynamic, access-control and access-permission objects;

FIG. 34 illustrates components of a RAMS system that manages status foruser objects, process objects and data elements;

FIGS. 35 and 36 illustrate assembly of an instance policy object basedon elements of local access policies;

FIGS. 37 and 38 illustrate an embodiment of use of a RAMS in the contextof organization of departments of a business;

FIG. 39 illustrates exchange of certificates among entity publishers andsubscribers;

FIG. 40 illustrates elements for management of propagation ofpublish-subscribe certificates;

FIG. 41 illustrates elements for management of dissemination ofcertificates based on global propagation parameters;

FIG. 42 illustrates association of associated reference data with anobject;

FIG. 43 illustrates associated reference data for child nodes of a node;

FIG. 44 illustrates elements of an embodiment involving modification ofaccess parameters and associated reference data for an object; and

FIG. 45 illustrates a system role hierarchy to which various levels ofaccess may be provided.

DETAILED DESCRIPTION

The Decision Time System (DTS) and its Assertional Simulation processes(and its variations) embody systematic transformation, improvement andintegration of various computer-implemented methods and solutions to anumber of practical problems. Among other things, DTS integrates inunique ways the functionality provided by a patchwork of disparatetechnologies into an integrated system (“DTS Platform” or “Platform”),and, deploying novel enhanced and integrated functionality, extendsthese capabilities within an expansive and systematic synthesis, therebyproviding new and previously unavailable capabilities while addressingexisting problem-sets in novel ways.

In embodiments, DTS traverses and encompasses an intersection of twomajor areas of technology: modeling/simulation and business/performanceanalytics. These areas share some technical, theoretic, and evenpractical use similarity; however, the distinct commercial andtechnological vectors that define modeling/simulation on the one handand business/performance analytics on the other bear only tangentialsimilarity to one another, share few tangible or required methodologiesand explicitly address few common problems.

It is in this light and based upon the practice-grounded origins and themulti-pronged applications of DTS that the teachings embodied in DTS(and in its various operational implementations) represent a departurefrom and an improvement of previous application areas, systems andcapabilities, most especially (but not exclusively) in respect tomodeling/simulation and business/performance analytic systems but alsowith respect to other ostensibly unrelated and varied application areas,systems and capabilities such as (without limitation) financial andtransaction and topical enablement and mediation systems; businessdevelopment and business initiative analytics; intra-organizationalinitiatives and opportunity analysis; prospective customer and salesanalysis; customer recruitment prioritization systems; cost accountingand operations analysis; computer, system and network access control,security and execution-rights mediation, and the like. The methods andsystems of DTS also reveal novel application of nominally unrelatedfields of pursuit, including, without limitation, machine learning,applied computational geometry, applied elements of set and categorymathematics, genetic and competitive programming, adaptive artificialintelligence (AI) systems and dynamic communication networks. Thisdeparture, as described in this disclosure and as may be understood bythose of skill in the related arts, encompasses differences inarchitecture, in the application of combinations of methods andprocedures, in the dynamic manner in which such methods and proceduresare contextually invoked and in the structural and organizationalunderpinnings, as well as in its practical application and usage.

In the most general statement of its core overall functionality, a DTSAssertional Simulation, and the constituent, ancillary and supportiveoperating components and interfaces (in variations) enable imposition,or projection of a formalized and structured informatic contention whichmay (without limit) comprise possibly diverse schematic structures (eacha schema), computational transformations, semantic changes in referenceframeworks of elements (including without limitation, transformativesemantic re-referencing and/or re-orientation of constituent informationelements) upon one or more reference targets, where such referencetargets are typically separate and distinct entities (such as, withoutlimitation, a hierarchical data structure with information content) toproduce a third entity, which embodies the result of the imposition ofthe contention upon the reference targets.

Assertional Simulation (also referred to as DTS Assertional Simulation)encompasses cumulative operations that simulate the effect of suchproposals upon such targets, thereby yielding one or more derivative andtypically separate and semantically distinct entities that comprisestructural alterations and/or compositional transformations and/orreference frame transformations (or other changes in structure and/orcomposition) effectuated by means of the joint application of one ormore of the elements composed within its precursor structures (theproposal and the reference target). Each of these separate resultantentities, or outcomes, may, in embodiments, reflect types and degrees ofcorrelation to its progenitors which, though at least partially disjointfrom one or more elements composing the semantical frame itsprogenitors, reflects a derived informatic relation which unifies, onthe one side, the original structure and semantic meaning (or referenceframe) of a composed or constructed assertion (where this assertion may,for example without limitation, be germane to a perceived truth of somematter relating to or subsumed within the structured target entity butwhere each entity may respectively exist within disjoint semanticalframeworks), and on the other side, the structure and semantic meaningembodied in the reference target, such that, as a result of theimposition (or coercion) of the assertion upon the reference target,this derived informatic relation serves as one basis of the schema andthe semantic frame used for the particular projection, a form offormalized opinion or judgment.

This characterization of the execution of an Assertional Simulationconveys a basic and general expression of the diverse and varied suiteof transformative, synthetic, normative and analytic operations appliedby and within a DTS Platform to execute various modalities of this novelvariation and extension of the art and science of simulation. Employinga complex of dynamically assembled and adaptive computationalfunctionality, Assertional Simulation executes a unique projection of aset of formalized parameterized assertions upon one or more targetreferences using, in embodiments, a form of informationalre-composition, one re-purposed within DTS. In DTS-terminology, thesecollective operations are referred to as “DTS Shannon entropicimposition”, language specifically employed within DTS to reflect thetangible, computationally executed “forced assertion or projection ofone informatic structure upon another”, an operation also referred to,in some cases as “coercive assertion”. (Note that the term “Shannonentropy” (and related terms) are used in this disclosure as defined ininformation theory and not in the colloquial sense (as related tothermodynamics). In embodiments and in general (but not exclusively),the transformational nature of DTS Shannon entropic imposition iscoercive in that, among other things, both the schemata and inheredsemantical bindings and boundaries defining the informatic referenceframe of the assertion is projected upon elements within the target(which may or may not have an entirely different schema and semanticalframework but which in any case may, in variations, be treated as suchwithin DTS) such that the structure and semantical frame embedded withinand defining the resulting outcome reflect the composite structure(s),schemata and compositional framework(s) which composed and defined itsantecedents but which is nonetheless distinct from either.

In embodiments, a DTS Platform provides methods and procedures andinterfaces that provide users (and/or system processes and/orcombinations) extended capabilities by means of which to create,organize, parameterize, duplicate, restructure, transform and otherwisemanipulate the aforementioned organizational and relational andgeometric schemata and/or the composition and semantical framework(s)(as well as other transformative and supplemental affiliates) thatdefine the elements subsumed and composed within an assertionalstructure, where these combined and possibly disjoint properties, inaggregate, give such assertional structures definition, form andcomposition. This collective DTS capability enables users (and/or systemprocesses) to embed within or formally imbue and/or organize orotherwise arrange collections of assertional structural andcompositional propositions, proposals and interpretations about thetarget (or the manner in which a target should be modified or shouldhave been modified), thereby composing a systematized and formalizedactualization of a point of view or judgement regarding one or moretarget structures, an informatic aggregation (or, in variations, two ormore aggregations) that cumulatively reflect combinations (withoutlimitation) of objective facts, quantities and transformations,interpretations about the form and application of objective facts,quantities and transformations, subjective judgements, interpretive andopinion-based application of varieties of domain expertise, but also,importantly, represent and permit users to crystallize and imbue suchstructures with distillations of experience and intuition and/orconsensus regarding any of the foregoing gathered from affiliatedparties or processes. This versatile, flexible and context-determinedschematic and compositional aggregation (and the systems by means ofwhich it may be actualized and manipulated) embodies a unique extensionand enhancement to these arts: a formalized structural and semanticbasis for the application of the aforementioned DTS Shannon entropicimposition (and its DTS-developed corollaries), noting, as well,however, that, in variations, the extended capabilities conferred bythese innovations may have broad additional application in non-DTSapplications and beyond DTS Assertional Simulation, most particularlywhen executed in conjunction with other DTS-unique, re-purposedcapabilities, as outlined in the following figures, descriptions andexamples.

DTS is also founded upon an unusual entity-centric and event-triggereddesign where each such formalization and the related and consequentaggregates of information (or sections therein) as well as one or moreelements within any of the ancestral progenitorial forms, its byproductsand its analytic results are owned by users, by groups of users and/orby system processes and/or by other data structures. Entity-centricownership is a tangible expression of a seminal architectural andoperational characteristic that is foundational to DTS Platforms: users,system processes and information aggregates are not only co-equal asoperating “entities” but may be (and in embodiments and, in somecontexts, must be) “owned” by another entity. Note, however, that invariations, the substantive and concrete results and consequences ofthis mandated entity inter- and intra- and cross-ownership may bemulti-leveled and conditional and, in variations, context-bound and maybe expressed in a number of modalities. Thus, users may fully orpartially and conditionally own other users, users may own data and mayown processes (again fully or partially and conditionally) but equally,processes may also own users and/or data, and so on. But entities alsopossess properties and characteristics (referred to as entity “ancillarydata” within this disclosure) and DTS provides extensive methods tomediate and arbitrate how the various rights and capabilities apply inoperational and functional contexts. In modalities and applications,such a layered ownership scheme may become complex: entities, forexample, may confederate into both transient and permanent associationsand may possess and assume different roles which may carry differentownership schemata and provenance. The management and administration andenforcement of this potentially complex framework is interwoventhroughout the methods and procedures and interfaces embodied within DTSPlatforms and represents not only a novel operational structure butenables and enhances many unique DTS capabilities, features andfunctions. The DTS-generated term that encompasses the collection ofDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces that execute and maintain this functionalityis “DTS Entity-Management Fabric” and its constituent elementsAssociational Process Management (APM), RAMS (Recombinant AccessMediation System) and Topical Capability Mediation (TCM), functionalcomponents within DTS that are described in the following paragraphs andin figures within this disclosure.

The practical consequences of the pervasive, system-wide coercion ofboth entity equality and the layered and context-adaptive ownershipscheme redound throughout a DTS Platform, improving and extending, forexample, execution modalities of Assertional Simulation and theoperations related to the creation and maintenance of elements thatcontribute to such operations but also provide additional, previouslyunavailable capabilities in access control and security operations.Equally, however, this unique approach also permits new and novelextensions throughout the DTS Platform, permitting, for example, broadexpansion of Assertional Simulation into new application arenas,enhancement and enrichment of the integrated competitive, collaborativeand comparative environments and the extension of the capability foruser (and system processes) to engage in detailed, fulsome andvariegated strategies in pursuit of various system-fostered goals.

In the context of the DTS entity relationships and ownership scheme,each formalization of an assertion embodies one or more forms of astructured contention or point of view promulgated by those owner(s)(for whatever reason) concerning one or more aspects of one or moreaspects within a target entity. These proposed transformations reflectand compose a coherent belief in the veracity of a perceived version of“ground truth” according to that owner's point of view. AssertionalSimulation executes the mathematical composition of these structureddeclarations upon target aggregates of information and produces ajointly derivative result that reflects the imposition (or projection orDTS Shannon entropic imposition) of that belief about “ground truth”upon the target entity.

This high-level functional portrayal of Assertional Simulation isreflected in a developed DTS-centric nomenclature, a set ofcontext-bound terms and groups of terms used for both convenience andprecision in the development and implementation and deployment ofinstantiations of DTS but also present in the following descriptions,illustrations and specifications of the actions executed within and byembodiments of DTS Platforms. As discussed, in the most general terms,the aforementioned user- or system process-associated proposal skeinrepresents a formalized point of view concerning how “ground truth”, asdefined by one or more entities (most often users) should be (or oughtto be) imposed upon or otherwise reflected within the structure and/orcomposition of a target entity. In DTS, this point of view is formallycomposed within and composes the central propositional characteristic ofan entity called an Assertion Model, which, as stated, is a DTS-centricterm referencing a structured informatic aggregation. An Assertion Modelmay be optionally combined with a distinct informatic aggregation calledin DTS terminology an Apportionment sub-Model, an adjunct reflective ofits “governing” Model (usually the Assertion Model) which providestransformative and/or scaling and/or apportioning informationstructurally correlated to and ontologically derivative from one or more“governing” Assertion Model(s). These combined entities (referred to inthe context of DTS as an Assertion-Apportionment Model pair or as anAssertion-Apportionment Frame) may be applied to thepreviously-referenced target entity which is called the Reference DataModel. The result of the application of a DTS Assertional Simulationusing these (and related) entities is called a DTS Outcome Model, aninformatic entity jointly derivative of and ontologically andstructurally reflective of the systematic projection (or the applicationof DTS Shannon entropic imposition) of the Assertion Model (andoptionally an Apportionment sub-Model) upon the Reference Data Model.

A DTS Platform operating environment supports a broad range ofextensions to and enhancements of the information processing andanalytic capabilities characteristic of Assertional Simulation. DTSprovides not only standard framework-like capabilities (such as APIsupport and a variety of standard user interfaces) but additionalcapabilities, such as, for example, without limit, specialized userinterfaces, services, features, and extensions providing the means tocreate, reconstitute, modify, enhance and otherwise manipulate AssertionModels and Reference Data Models and elements thereof, and to similarlymodify and parameterize Apportionment sub-Models; methods and proceduresand interfaces that, in variations, enable DTS-based, DTS-enabled,DTS-accessible and system-external analytic computational activity to beapplied: to one or more DTS models and one or more elements thereof; toinformation that may be composed within discrete DTS models and relatedinformatic structures and elements that may be inferred from suchentities as well as to combination of other DTS-based and/or DTS-enabledand/or DTS-accessible analytic products and activities.

The following description details various embodiments and variationscomposing a DTS Platform and the implementations and variations of themethods and procedures by means of DTS Assertional Simulation andcontains examples, narratives and supporting figures that illustrateembodiments of the broadly-based, real-world applicability of DTSAssertional Simulation and associated DTS Platforms. The versatility ofthe design and the novel combination of functionality and capabilitiesthat may be applied to and which surround and extend and enhance DTSAssertional Simulation provides the basis for this wide-spread utility.

DTS Assertional Simulation and the broad collection of DTS-based (andDTS-enabled and DTS-accessible) components, methods and proceduresprovided within and by means of a DTS Platform (outlined in thefollowing description) find great value across a broad range ofcircumstances and enterprises where users or groups of users advocatealternative points of view and priorities based upon differing opinions,judgements and perceptions. A sampling of such circumstances where thefeatures and functions provided by and within DTS Platforms include(without limitation): (i) enterprises wherein users or groups of usersmay debate how their organization or entities within that organizationentity should be organized and, further, how organization- andentity-related information or operational processes should be (or shouldhave been) structured, evaluated and, ultimately acted upon; (ii)organizations where decisions are routinely made concerning howprojects, initiatives and other developmental activities should beevaluated, pursued, prioritized and configured, in what way the requiredresources should be deployed and how related costs should be estimated,allocated, distributed and even sequenced over time; (iii) exchanges andmarketplaces and other transaction-based environments where users andgroups of users on opposing sides of a transaction reciprocally engagein a variety of transaction-related activities including (without limit)internal debates about analysis and valuation of elements composing thetransaction, evaluation of the importance and degree of exposure torisk, status of contingencies, prevalence of market hazards and otherdomain-related considerations where each participant on the respectivesides of the transaction typically opines from a position of interest inthe transaction, but in general advocates positions definitionally basedupon or influenced by judgement, opinion and orientation and which arefurther informed by degrees of experience and extent of domainknowledge, and which involve subjects that are multi-disciplined,complex, nuanced and layered; (iv) strategic planning environments wheredifferences in perception of the relative value and the consequences ofchoices may be expressed in qualitative and quantitative frameworks,where such diverse factors need to be formatted, revised, framed,visualized, presented, evaluated and reconciled; (v) commercial andnon-commercial organizations in all sectors and of all sizes andcomplexity operating both simple and complex accounting systems wherethe actual consumption and optimal allocation of resources is a commonand contested topic debated in many contexts involving such issues as(without limit) profit center evaluation, cash flow assessment andbudgeting and forecasting exercises—these instances cite but a few ofthe areas in which the novel capabilities of the current invention maybe employed.

In embodiments, a DTS Platform provides an integrated operatingenvironment wherein users may construct, present and advocatepropositions, proposals and solutions (as embodied within bothuser-generated and (optionally) system-enhanced) Assertional Simulationsand may thereby obtain the means to actualize and make tangible andpresent a variety of alternative choices, versions of opinions andproposals. In this context, competing and even adjunctive propositionsmay be presented, examined, subjected to debate, analysis andevaluation. This collection of DTS-provided capabilities and activitiesenables and enhances the competitive, comparative and collaborativeactivities and environments that commonly exist in many professional(and even personal) settings and organizations where alternate points ofview are not only held and advocated by individuals and groups ofindividuals but are also are presented and debated, often in anunsystematic way. Utilizing methods and procedures and interfacesprovided within (or enabled by) DTS Platforms, elements of and resultsfrom various types of DTS Assertional Simulation (as embodied in OutcomeModels and other ancillary information) incorporate these propositionsin a systematic operational skein so that solutions may be examined, maybe subjected to further analysis and interpretation, tweaked andrepeatedly simulated, dynamically modified and tested and compared andcontrasted to collections of results spawned by application of otherAssertional Simulations, possibly originating from and advocated byother users or groups of users. A DTS Platform not only formalizespoints of view within Assertion-Apportionment Frames constructed byusers to reflect their belief about a Reference Data Model, but alsoformalizes the means by which such alternate views may be compared.Thus, DTS Platforms provide a suite of competitive and collaborative andcomparative capabilities explicitly designed and enabled to facilitatenot only inter-user propositions but which also permits users tocontinually construct, modify, test, combine, duplicate and perfecttheir own Assertion Model/Apportionment sub-Model/Reference DataModel/Outcome Model sets. Such layered evaluative capabilities withinembodiments of DTS Platforms comprise but one application of thevariegated types of activities surrounding Assertional Simulation thatare available to Platform users, but one that extends and enhances theutility and application of Assertional Simulation.

The competitive, collaborative and comparative component of DTS and itsDTS-based (and DTS-enabled and DTS-accessible) extensions and the use ofthe varieties of constituent Assertional Simulations combine to supporta constellation of activities surrounding advocacy, and thisbroadly-based capability directly addresses common and vital activitiesencountered in nearly all aspects of business and government. Managersaround the world, for example, in businesses of all sizes and types,must routinely make operational and financial decisions in pursuit ofboth simple and complex strategic and tactical goals. In many suchinstances, participants must balance numerous alternatives withinterrelated levels of consideration, some nuanced, some extending overtime, some integrating cross-discipline and inter-domain specificity andsome requiring consideration of both quantitative and qualitativefactors.

One distinguishing feature in this common managerial exercise, however,is that much of the decision calculus is infused with, informed by andinfluenced by the opinions, judgements, expertise and experience of therespective advocate(s). But as a measure of the novel approach in DTS,the subjective activity associated with these common operational andinteractional paradigms are, as described in the foregoing, formallycaptured, framed and structured by a DTS Platform as assertions andreference targets—that is, as collections of Assertion Models, ReferenceData Models and the associated Outcome Models. But more, these Modelsthat crystallize these common decision paradigms are made available to,are supported by and are enhanced and extended as a result ofapplication of a wide variety of ancillary and supportive capabilitieswithin a DTS Platform.

Note, however, that the nature of Assertional Simulation is not onlyfundamentally analytic but provides a new type of predictive analytic.In particular, the production of Outcome Models provides a novel,multi-part and context-sensitive predicative capability as part of itsoverall analytic overlay, both of which are fundamental results andcharacteristics of Assertional Simulation. The use of AssertionalSimulation as a tool for the probabilistic prediction of the past,present and future veracity and efficaciousness of atransformed-by-assertion Reference Data Model (as embodied within anOutcome Model) yields a variety of context- and content-sensitiveresults which are useful across many applications and in manyenvironments.

The predictive analytic nature of Assertional Simulation is simple andclear to see when considering that, as stated, the objective ofAssertional Simulation is to produce a “better” representation of thereality encoded in a Reference Data Model. Thus, users predict that thechoices they make within their Assertion Model will not only result inthe desired improvement (as they understand it) but that theparametrization of this admixture of facts, opinions, judgments andexpertise within the Assertion-Apportionment pair is the best means todo so. Thus, by its nature, the quantitative and subjective choicesimbued and initialized by users or system processes withinAssertion-Apportionment pairs are chosen to maximize the probabilitythat the Outcome Model will comprise the best representation of theirvision of what reflects a “good” or “true” or, at least, their desiredset of changes to a reality encoded in the Reference Data Model. This isa new type of predictive analytic, one that incorporates some of thenovel aspects of DTS but which also, in variations, extends beyond thissimple example.

Thus, in embodiments, a DTS Platform may be used both as an analysismechanism in itself and an enablement facility for analysis creating theconditions for presentation, explication and resolution of manypractical and immediate business enterprise problems where importantaspects of the advocacy and decision-frames are opinion-, experience-and judgement-based, such as (without limitation) optimal profit centerorganization; actual cost of resource deployment for projects andinitiatives; actual cost to pursue new initiatives, to develop newbusiness and to extend the scope and operations of existing businessunits; indirect cost allocation among operating units and profitcenters; contextual and dynamic assessment and assignment of employeecosts; integration of the foregoing into budgeting and forecastproposals; creation and comparison and analysis of varieties of“what-if” scenarios, use of proceeds and evaluation of initiatives andmany other day-in-day-out activities. These are common activities whichmanagers of all levels in small and large organizations of all typesregularly engage in, and they share the common feature that opinion,experience and judgement influence and often play a key role in theresults. A DTS Platform is specifically designed to use various forms ofAssertional Simulation and to deploy a broadly-based and versatile suiteof ancillary and support functionality to provide an integratedenvironment and solution-set but to also facilitate the novel predictiveaspects of Assertional Simulation to be applied to such real-wordproblems.

As one simple example among many, suppose a group of managers within anenterprise have different views about how the enterprise should beorganized, such as with respect to its organizational chart. Thesedifferent points of view held by the managers (and the underlyingrationale and supporting logic) may be reflected in one or morepropositions that can be structured and applied by each party via astep-by-step (and in implementations, system-guided) process ofconstruction and parametrization of one or more Assertion Model(s) andApportionment sub-Model(s), through the structural assembly andcompositional construction of Reference Data Model(s) and application ofDTS Assertional Simulation, processes executed by each manager. Ingeneral (but not exclusively), common (or shared) Reference Data Modelsare used by all parties and may (typically but again not exclusively) beextracted or derived from one or more existing data structures availableto the management group from within the enterprise (such as operationalor accounting data). These elements permit each party to createcollections of Outcome Models that reflect that party's various pointsor views and opinion pertaining to one or more potential or optimalresults. These collections of results-sets (and the progenitorialstructures and ancillary information from which they were derived) maybe used to arrive at a collectively agreed-upon solution but in theprocess of arriving at this consensus, these DTS-fostered processes andthe constituent structures may reveal insights about the enterprise andeven about the participating parties, inferences and conclusions thatare drawn from, based upon and made possible by the DTS operations thatpermit formalization, comparison, modification, presentation andanalysis of the alternative perspectives. Among many examples,enterprise structures involving organizational charts, reportingchannels, communication channels, charts of account, allocations ofprofits and losses, allocations of costs, allocations of assets, andmany other common elements in enterprise operations may benefit fromAssertional Simulations and the capabilities provided by a DTS Platform.

The breadth and variety of enterprise-centric, operational, financialand commerce-based applications of the DTS Platform reveal one aspect ofits novel design; namely that DTS Assertional Simulation and theassociated DTS Platform-based functionalities can (in variousimplementations) be operated in multiple modes and orientations, in somecases transitioning between and employing such modes dynamically andwithin computational and informatic compositional contexts, and invariations, transparently to users, without requiring direct (or evenindirect or even any) user prompting. Certain Assertional Simulationapplications, by their nature, combine certain modes.

A user, for example, may invoke certain permutations of an AssertionalSimulation. In embodiments, these permutations may have beenpre-built-into a DTS Platform or assembled, named and saved by user(s)).In the process of construction of permutations, one or more versions ofAssertion Model(s), Apportionment sub-Model(s) and permutations ofReference Data Models may (without limitation) be permanently,dynamically or deterministically arranged, layered, sequenced,re-sequenced and/or rearranged in one or more pre-set or conditional ordeterminative computational orders. Such construction procedures may(without limitation) produce or derive and reuse one or more versions ofresultant Models. In embodiments, the computational objective ofpermutation construction and/or supplied parameters may be optionallybased upon, inferred from or derived from any or all of the following(in any order and without limitations): (i) user and/or system and/orextra-system input; (ii) information supplied by and/or derived fromlocal and/or global and/or extra-system conditions; (iii) data and/ormetadata and/or other transformative parameters extracted from and/orproduced from or by one or more current or previous execution results(and/or by and from one or more elements therein). In embodiments, thisinformation may be utilized conditionally or deterministically toproduce one or more Outcome Models such that any of all of these resultsmay be presented, analyzed and visualized in a variety of ways but suchthat the net operation may be understood in user-terms as composing asingle operation. In plainer language, a DTS Platform permits users tocombine and instigate Assertional Simulation by assembling collectionsof (and elements from) Assertion Models (and Assertion-ApportionmentModel Pairs) from a variety for sources and in a variety of ways, evenfrom previously derived (and possibly discarded) results-sets. Theresults of such combinatorial modes of composite assembly may alsoinclude the antecedent processes that produce the results and may bepreserved for reuse or for reference or for any reason (such as, forexample for inclusion in an audit trail of activity). Moreover, in othervariations, such operations as in the foregoing (e.g., as composedwithin methods, procedures and interfaces within a DTS Platform) mayexecute functionality outside such composite operations but which may(with or without user prompting) transiently assemble elements as a“composite confederation” and execute all or part of such compoundAssertional Simulations, where the aggregation is dynamically actuatedand available in any number of computational contexts, where suchaggregations may conjunctively, adjunctively and conditionally ordeterministically also (without limitation) execute and incorporate oneor more ancillary and supportive operations, thereby assemblingcombinations of capabilities to execute and leverage the constructiveand analytic capabilities of an Assertional Simulation.

Among other advantages, the dynamic, adaptive and multi-modal, layeredexecution architecture of the DTS Platform and its constituent elementsnot only extends and enhances the inherent capabilities of AssertionalSimulation (and its ancillary and supportive operations) but alsopermits and extends broad application in commercial, non-profit,personal and governmental applications. In embodiments, thisarchitecture hides much of the complexity from users and operators andpermits users of all levels of expertise to seamlessly (and oftentransparently) adapt to and transition to and transition from,transition within and transition between a variety of functional andoperational modes and conditions.

One versatile capability that is broadly-deployed within variousoperations within the DTS Platform (e.g., operations where itsmulti-modal capabilities may be evident) centers about comparison,differential analysis and competition between ideas and proposals andother by-products of Assertional Simulation. These capabilities applynot only within organizations that may be assessing the relative valueof various initiatives but also between market competitors such as, forexample, competitors that may be involved in transactions within marketexchanges. In such undertakings, and indeed in many commercial andnon-commercial endeavors, competing and alternate viewpoints about valueand relative value of certain propositions (and even of goods,commodities or services) often form the basis of internal deliberationand debate in the process of decision-making (e.g., in the case ofenterprise-based operations) and competitive bidding and negotiation(e.g., in markets for goods and services).

Note, however, that in business and in governmental enterprises,managers not only compete for resources but also advocate particularviews of ground truth and put forward opinions (often underscored andreinforced by quantitative factors and arguments) about how theinterests of the organization (or their part of it) may be pushedforward by selecting one proposal (or approach) over another. In manyinstances, such a proposal may combine objective facts and quantities,assert interpretations about how to use such data and advocate beliefsabout its relative importance, thus creating an inherent subjectivity, aperspective underscored by the common inclusion of subjectivejudgements, interpretive application of domain expertise and opinionculled from experience and intuition. These situations and other commonenvironments embody at least one application domain of AssertionalSimulation. The DTS Platform combines many disciplines and diversetechniques and transforms and interconnects nominally unrelatedcomponents to enable systematic methods that permit users of all typesand levels of expertise to address and solve these problems.

The DTS-based (or DTS-accessible or DTS-enabled) components, methods andprocedures that comprise the DTS Platform may be employed in variousmodes and novel combinations, and, as a consequence, the Platform andvariations of Assertional Simulation find utility and applicability inmany aspects of commerce and enterprise operations. As an example, themethods and procedures permitting the projection of a domain of assertedpropositions across a domain of reference targets to produce an outcomethat may be evaluated and compared in order to find such utility andapplicability. The following description will elucidate but a few suchapplications, and those skilled in the relevant arts may plainly seeothers not here described.

Note, further that it is characteristic of embodiments of AssertionalSimulation and the DTS Platform in general that, in variations, thespecific operations used to execute certain types and classes ofcalculations (and other related operations) may utilize any number ofcomputational techniques, depending upon any or all of the followingconsiderations without limitation: structural and compositional factors;operational, procedural and computational context; explicit and inferreduser preferences, user activity history and other context-basedactivity-based operations; explicit and implied user mandate; and systemrequirements, limitations or constraints. Thus, for example, operationsexecuted within any given Assertional Simulation (or as may be executedwithin an iteration composed with a sequence of such simulations) mayadapt to and may be altered or otherwise adjusted in response to theimmediate (that is, the current) system state, conditions and context,and/or to previous states and conditions, and in variations, toprojected future states and conditions, where such states and conditionsand context may include but may not be limited to any combination of thefollowing factors: (i) one or more aspects related to (or which may beinferred from) the content of the data being processed; (ii) one or moreaspects related to (or which may be inferred from) the geometricstructure and relationships to other information; (ii) user and/orsystem mandates and intentionality; and/or (iv) specific requirements orconstraints that may result from any of the foregoing but also from oneor more aspects of a specific computational executable.

In one example, presented without loss of general applicability ofalternative methods, Assertional Simulation may, in variations and insome circumstances, execute a node-by-node, discrete and/or piecewiseinformational convolution between elements composing proposed assertionor declaration structures and elements within targets (in DTSterminology, this operation is called DTS informatic convolution todistinguish this DTS-engendered coercion from other forms of convolutionwhich, in variations, may also be applied within DTS implementations),wherein one or more nodes within the proposed schematic geometry(composing the structured assertions) may be applied upon one or morenodes within target entities, such that one or more DTS executablemethodologies collectively produce structurally and ontologicallycorrelated derivative and logically discrete result entities composedwithin designated, distinct and addressable informatic entities. In DTS,these derivative structures are called Outcome Models and embody andembed the mutual information jointly extracted from and reflective oftheir particular proposition-target precursors.

As one use-case example of this specialized type of informaticconvolution (which is, itself a novel use of the well-known convolutionoperator seen in linear mathematics—again, referenced with theDTS-spawned term DTS informatic convolution), consider the applicationof Assertional Simulation to a common problem in operations: theallocation of indirect expenses to profit centers. Accurate reflectionof how resources associated with these profit centers are actuallyconsumed (as opposed to a formulaic estimation often applied byaccounting personnel and rarely changed) is vital in assessingprofitability throughout a company but is very often a matter ofjudgment and opinion. Managers, for example, in charge of profit centersthat use resources shared with other such centers such as, for example,a conference room, may offer different opinions about true usage bytheir personnel within their profit centers. To illustrate the manner inwhich Assertional Simulation may be applied here, suppose there are 6profit centers within a section of a company with 6 managers in chargeof their budgets and responsible for their profitability. Let DepartmentA have fewer personnel than the others but, in this example, uses aconference room much more often than Department B which has moreemployees assigned to it, while Departments C though E (whose headcountlies between the Departments A and B) barely use the room at all, whileDepartment F, which has the highest employee headcount, never uses it.And yet the cost of maintaining the room (designated as an indirectcost) is allocated across the 6 departments using a formula set up andmaintained by a remote accounting department which does not itself usethe resource nor witness its usage. This situation is common inorganizations of all types and sizes. In the current example, theindirect costs accrued for maintaining the conference room areformulaically spread by headcount among the 6 Departments, also a commonpractice. But Department A is the most profitable among this group whileDepartment F is the least. By deploying DTS Assertional Simulation, thecost of the conference room (contained in accounting data and reflectedin the relevant Chart of Accounts entries) may be made to serve as theReference Data Model. Each manager may then assemble their ownAssertion-Apportion Model pairs wherein each embeds their opinion abouthow their respective Departments utilize the conference room—that is,they assign a percentage, by month, for each Department within theirAssertion Models. Each manager executes an Assertional Simulationproducing a set of Outcome Models, each reflecting the view each managerhas of the proper apportionment of the cost of that resource to theirDepartment. In this example, using the competitive and collaborativecapabilities within a DTS platform, the various points of view (asembodied in the aforementioned Models and Outcomes) are examined andanalyzed and, in this case, it develops that in reality Department Auses the room 70% of the time but is only assigned about 16% of the costby the headcount-based formulaic allocation of the total costs for theconference room, whereas Department F consumes 0% of the resource but isalso assigned 30% of the cost. The other Departments discover similardisparities.

In this instance, the managers and their supervisors agree to adjust theallocations and the result is that not only does the accounting of theconsumption of this resource more accurately reflect the actual “groundtruth” but the profitability of each department changes, as well. Thus,a set of conclusions were derived through consensus by a group ofstakeholders, each creating, parameterizing and executing their ownAssertional Simulations, producing unique-to-them Outcomes, informationthat could be compared and analyzed. There are many ways that a DTSPlatform may assist in and augment and enhance even a simple AssertionalSimulation exercise like this, and such capabilities and relevantexamples will follow in this description facilities.

In its most essential expression, therefore, DTS Assertional Simulationformalizes the imposition or coercion of structured user-perceived,user-nominated (or in some contexts system-driven) “ground truths”, aDTS-specific term used to encompass and describe not only verifiablefacts, but which may also include points-of-view about such facts wheresuch perceptions may differ between system users and even within andbetween system processes and information structures. Such coercion of anassertion of “ground truth” is applied by application of dynamicallyinvokable content-sensitive and content-responsive computationalfunctions upon targeted information sources such that the consequentresults simulate such imposition or coercion upon a target. Result-sets,in turn, embed the precepts that were embedded within the declarativeassertions (and applied by projection to target structures) and are (orshould be, according to the asserted ground truth) valid in theinformatic context of the target. Thus, in this most general statementof its overall functionality, DTS and Assertional Simulation (and thesupporting and ancillary components described in this disclosure)enables imposition, or projection of one or more proposed schematicstructures and additional related scaling or apportioning information(the Assertion Model and Apportionment sub-Models—often collectivelyreferred to in DTS terminology as the Assertion-Apportionment pair) uponinformation elements within a target (the Reference Data Models), atypically separate, structured entity (such as, without limitation, ahierarchical data structure with information content). AssertionalSimulation encompasses cumulative operations that simulate the effect ofthe imposing the schemata and affiliated information within anAssertion-Apportionment pair upon such targets, yielding one or morederivative and typically separate entities (Outcome Models) that, insome modalities, reflect structural and/or compositional and/ornormative and/or translational re-composition derived from itsprecursors. Each of these separate entities, or Outcome Models, reflectsthe broadly-defined semantic meaning or intentionality which reflects,as in the foregoing, a point of view stipulated by users and/or systemprocesses about one or more aspects within the Reference Data Model,where such semantic meaning or intentionality is embedded within anAssertion-Apportionment pair. In DTS terminology, this characterizationof Assertional Simulation is referred to as a Shannon entropicimposition.

In embodiments, therefore, DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces permit users(and/or system processes) to create, organize, parameterize, restructureand otherwise manipulate the aforementioned informatic structures inorder to embed propositions, proposals and interpretations about thetarget, a systematized and formalized point of view which forms thestructural and semantic basis for the aforementioned entropicimposition; in DTS, each such formalization may be associated with andowned by users, by groups of users and/or by system processes and mayembody the owner's structured contention concerning the appropriatecomposition of one or more aspects within a target entity, an assertedveracity that reflects the ground truth according to a given point ofview. The functional composition of these structured declarations upontarget entities produces a jointly derivative and correlated resultreflecting the imposition (or projection) of the ground truthpropositions upon the target entity. Among other applications, DTSAssertional Simulation is of great value in situations where there maybe alternative points of view about how an entity or its associatedinformation or processes should be structured.

In practice, this collection of methods and procedures produces novelinformatic structures that, in implementations, may be used in numerousways by DTS users and, by others (by means of API's and formattingprocedures), by DTS-based and DTS-enabled system processes as well as byother external resources. Further, the result-entities from AssertionalSimulations may be used in a number of ways, including but not limitedto: (i) as standalone analytical products that reveal the consequence ofthe imposition of a particular set of truths or points-of-view upontargets, surfacing, in many instances and variations information orinsights that may have been previously obscured; and (ii) as tangiblere-structured admixtures of information elements that are systematicallyderived from the progenitorial entities and that are recast based uponuser-driven (and in variations, system-mandated) points-of-view (therebycrystallizing the outcome of applying structured assertions upon theinformation involved in a given situation).

In this sense, in some variations, DTS Assertional Simulationresults-sets (and byproducts of its operations) may, in variations, beused in a variety of ways and may be used to serve multiple purposes,and in many cases, specific utilization choices may depend upon (or maybe influenced by) such example factors as user intention, data type orstructure. Most fundamentally, however, in a common but elemental usage,the result sets produced by DTS Assertional Simulation (and invariations, sub-sections thereof) may themselves compose the desiredend-point analytic. That is, in many applications, one or more resultsets (in part or in whole) provide users (or system processes) withsufficient information for the current task-at-hand such that thedelivery of the output of immediate operations requires no furthercomputation or processing: the “Outcome Models” produced by theapplication of DTS Assertional Simulation produces, in these cases, theinformation required by the system users. Thus, in such instances, theresults themselves constitute an analytic product and, in these andsimilar instances, Assertional Simulation functions as a complete andsufficient analysis tool. In such variations, these results may, forexample, and by means of one or more DTS-provided or DTS-enabled userinterface components, be rendered into graphical visualizations whichmay then be subject to visual inspection or display and didacticalanalysis.

The utilization possibilities for the results of Assertional Simulationare varied, however, and even in such cases where the immediateresult-sets serve useful (and in some cases) definitive analyticend-points, elements of the simulation products (and in variations, theprogenitorial model structures, as well, and, in other instances,sections thereof) may be subject to further DTS-based, DTS-enabled andnon-DTS analytic processing. In such instances and in someimplementations, DTS result-sets (and/or subsections thereof) may, inaddition to composition by means of various Assertional Simulationtechniques, be optionally re-formatted specifically to serve as inputinto one or more additional analytic processing resources, where suchcomputational facilities may be enabled by, may be available to or maybe integrated within DTS. Thus, in implementations, DTS results sets mayconstitute one (or one of several) input sources to one or more suchprocesses, where, in some variations, other factors may optionally beincluded within these computations (where these input values mayinclude, for example, DTS-originated or user-supplied or externallyacquired information, or combinations thereof), but such that in theseadditional processing cases, the cumulative effect would produce one ormore distinct analytical products, distinct that is, from the originalresults sets.

A simple initial example of a certain type of application of AssertionalSimulation, one to be expanded throughout the following descriptions asillustrative of one variety of the host of possible applications of thepresent invention, suppose in a simple market transaction, a sellerwishes to sell an item. (“Sellers” are called “seekers” in DTS parlancein order to permit its general applicability to many party-counterpartysituations, but for the sake of simplicity in the present example, willbe called “seller”.) Suppose further that within this marketplace, thereare groups of potentially interested buyers for this item. (Note againthat in DTS phraseology, “buyers” are called “providers”, where, asbefore, the term reflects the general applicability of DTS tocounterparty situations—so that, in a simple instance, buyers areunderstood as “providing” money, but, as may be seen as this type ofapplication evolves in the context of the following descriptions, couldalso effectuate a transaction by combinations of currency and othernegotiable items such as services or future considerations.)

Continuing this example, the seller, of course, has an opinion about themarket value of the item, but this is not always the sole determiningfactor in party-counterparty interactions. Suppose, therefore, that inthis example, the seller has a belief which qualities he would like tosee in the ideal counterparty. Thus, in this case, the seller wishes toknow certain details about the buyers in order to assess such things astheir honesty in other transactions, their histories and their statedintentions versus their actions. There are many possible reasons forthis common desire to know more about a buyer: the seller may not wishto see his item re-sold to a competitor, for example, and since (in thiscase) the seller is unable to prohibit a resale, may wish to evaluatewhich buyers are most likely to do so. Thus, in addition to price, theseller will wish to evaluate the relative “fitness-as-buyer” for eachprospective counterparty based upon on things like “resale likelihood”as well as other on other criteria. Note, therefore, that in thisexample, the seller may gather information from and about each buyer andassemble and organize such information into a structured informaticentity using DTS-based, (and DTS-enabled and DTS-accessible) methods andprocedures, a structure that represents, as in the foregoing, thereference target associated with that buyer, but which is owned andcontrolled by the seller.

Employing capabilities within the DTS Platform, the seller may alsoconstruct an assertional structure in order to evaluate this collectionof buyer-associated target references. These reference targets maycontain a mixture of quantitative and qualitative information, so theseller may use DTS capabilities to apply not only formulaic andcomputational evaluations but to assign relative weight to these resultsas well as to certain “intangible” factors, such as, for example, theseller's opinions and judgements about the importance of certaintransaction history and the likelihood of dishonest behavior. Thus, theseller now possesses a target reference for each buyer and anassertional structure by means of which to evaluate each such reference.

Note that for the sake of simplicity in the example, a large amount ofthe relevant information about buyers and sellers is available to andcommonly shared within the exchange, perhaps in this example, as part ofthe terms within the market exchange, and perhaps, as well since it ispublic information.

Continuing the example, suppose further that each buyer will also useDTS-based, (and DTS-enabled and DTS-accessible) methods and proceduresto construct a target reference applicable to the seller and then anassertion reflecting his views of the item, his priorities and otherjudgements unique to him. But note that since the buyer is on theopposite side of the transaction, the configuration ofreference-assertion pairs will differ from that constructed by theseller, even while some of the same considerations and judgements mayapply. In an example of reference-assertion pair differences, bothbuyers and sellers may have opinions (as expressed in each participant'sassertional structure) about installation costs (a common value that isexpressed in both participant's target reference); however, theiropinions may be formed from different basis. In constructing his targetreference, for example, (whose topic in this example is both the itemand the seller), the buyer will wish to perform an evaluation of thevalue of an item, one that will, of course, differ from buyer-to-buyerbased on any number of factors and motivations but which, in thisexample, may, in some instances be based not only upon the (perceived)intrinsic value but also upon other factors indirectly related to theitem itself. There could be many reasons for this common occurrence: aswith the seller, some buyers may have interests beyond the detailsgermane to the item itself and the mechanics and motivations of the saleitself—namely, an interest in details about the seller: what are themotivations behind this sale, for example, what sort of transactionhistory (buying and selling) does this seller have, how well have othertransactions gone, and so on. In most market environments, suchevaluative issues assume relative degrees of importance to variousbuyers, and, in many markets and/or in many transactions may rise to adispositive and determinative role in both the decision to engage with aseller and in setting a value for transacted item(s). Thus, as with theseller, buyers may create a reference target integrating informationrelated to both the item and the seller, including, perhaps,quantitative (e.g., size and the like) as well as qualitativeinformation (e.g., user ratings and the like) about the item itself,information related to the item (e.g., average installation costs andthe like) and information about and related to the seller.

As with the sellers, the buyers, may also create their own assertionstructures to apply to the seller-associated reference structure, wheresuch buyer-centric assertions may in concept be similar to those createdby sellers (as described above) and may use some of the same DTScomponents but which may differ both in intention and objective. Inaddition to “hard” numerical evaluation, the buyer's opinion, judgementsand experience about the value and relative importance of both “hard”and soft” factors are embedded in this structure as the buyer evaluatesdifferent aspects of the seller's associated reference target. But notethat each buyer is applying their individual assertions to the sameseller target. In DTS, this form of Assertional Simulation is calledunderwriting.

As may be seen in this simplified explanation, sellers and buyers mayuse the DTS Platform to execute Assertional Simulation, but each side inthis party-counter-party arrangement uses the Platform in a differentmode and from a different perspective and with different motivations. Inthis simplified example, the seller constructs a single assertion toevaluate a group of buyers each of which is assigned a collection ofassembled reference (or target) information; the collection of buyers,on the other hand, use individually-created (and buyer-owned) assertionsto evaluate a single reference associated with the seller. Note that inthis example, a large portion of the information assigned to both targetreferences of both buyers and sellers are shared within the exchange, itis also clear that even in this basic example, some additionalinformation may be obtained by some buyers and not others. In thisinstance, some uniquely-held information may be embedded in a givenbuyer's uniquely owned assertion structure. This disparity in“intelligence” capability is not uncommon, and DTS Platform operationsboth permits and enable such eventualities. Thus, in party-counterpartyapplications, each side of the transaction has a completely differentset of opinions and tasks: the seller wishes to evaluate and rank anumber of buyers using a set of criteria, while a multiplicity of buyersseeks to evaluate both the item itself but, less tangibly, in manyinstances, the seller. This simple situation reveals one multi-modalapplication of Assertional Simulation: both parties are engaged in thesame enterprise or “game” (to sell the item) but each side of thetransaction deploys variations of the Assertional Simulationcapabilities in a different mode and manner, each using differentcomponents, methods and procedures within (or enabled by or accessibleto) the DTS Platform but with same objective: to instantiate a set ofassertions and to project or cast or coerce that information uponreference target information.

Note, therefore, that since the seller may construct and embed a set ofcriteria within a single assertion and that this assertion is projectedacross many reference targets (e.g., buyers), each such AssertionalSimulation yields a unique result, a collection of outcomes (e.g., onefor each buyer). Employing a variety of DTS-based (or DTS-accessible orDTS-enabled) components, methods and procedures, each such result may be“ranked” against the others and subject to analysis and evaluation. Theseller may wish to “tweak” or modify variables and elements within theassertion and re-run the simulation, thus producing a different set ofoutcomes. This competitive, comparative operation (one assertion appliedto many reference targets to yield a field of outcomes) is a distinctmode within the DTS Platform and is based upon game-theoretic operationsto produce a non-zero-sum outcome wherein each projection operation is agame instance but there are only relative winners—hence, the net resultis relative ranking. In a practical sense, the seller is using DTSAssertional Simulation to run a private competition among buyers toevaluate the relative “fitness” of each buyer. In DTS parlance, thistype of Assertional Simulation is called “ranking”, typically (but notexclusively) deployed in party-counterparty application: in suchapplications, ranking is (in general but not exclusively) executed froma seller's point of view such that a single assertion is applied to amultiplicity of reference targets and the associated outcomes of therepeated simulation are ranked based upon the elements composed withinthe assertion cast across the reference target.

On the other side, in this present example, the buyers use a differentmode within the DTS Platform, called “underwriting”, also typically (butnot exclusively) deployed in party-counterparty applications: whereunderwriting is (in general but not exclusively) executed from a buyer'spoint of view. In underwriting, in contrast to ranking, AssertionalSimulation applies many assertions (each owned by a buyer or group ofbuyers) to a single reference target (the amalgamation ofcommonly-shared information about the seller), resulting in a uniquebuyer-owned outcome. In this example where there is a single item (andthus a single reference target), the results of underwriting areavailable for analysis and evaluation by the buyer. But suppose thisexample included multiple sellers of the same item or similar items (oritems that may be dissimilar but sufficiently alike as to be associatedin a market or section of a market). This change shifts thegame-theoretic activity of the buyers to a non-zero-sum ranking gamethat is similar in structure to that executed by sellers whereindividual sellers and their associated items are underwritten, and theoutcomes sorted by whatever criteria the buyers wishes.

Note that this example of Assertional Simulation is but one instance ofthe ways in which the DTS Platform may be utilized and illustrates, in ageneral way, multi-modal and “layered” game-theoretic operation providedby the Platform. In this instance, note that while each party is engagedin a non-zero-sum game in order to analyze and evaluate the transaction(sellers execute ranking of buyers and buyers execute underwriting ofthe sellers), the overall activity in DTS party-counterparty operationsis zero-sum since, ultimately, there is single buyer who purchases theitem. Thus, in embodiments, the competitive and collaborativeenvironment within the DTS Platform facilitates a number ofgame-theoretic based operations that may be combined, repeated andlayered within one another. In the present example, opposing partiesproduce outcomes based upon the results of Assertional Simulations andemploy in non-zero-sum methods to evaluate and analyze the relativevalue of each entity, relative ordering based on each players judgement,intuition, application of expertise to objective quantifiableinformation and other subjective criteria. But this bi-partite game is asub-game within a zero-sum, winner-take-all activity, where one buyer“wins”. This layered and combinatorial feature suffuses the DTP platformand provides but one of the supporting enhancements to the methods andprocedures of Assertion Projection.

This novel multi-modal and “layered” game-theoretic functionality is aunique characteristic of DTS, one executed within and by means ofoperations and interfaces throughout the DTS platform and in the variedapplication of Assertional Simulation and its results. This approach isnot merely a framework but is tangibly evident within many operationsand interfaces throughout DTS. A few examples of these capabilitiesimplemented in embodiments of DTS include (but are not limited to): i)the layered and competitive algorithms within one or more loopedrecursive operations (discussed in subsequent paragraphs) whichspecifically reflect certain precepts and parameterization techniquesderived from known game theory models; ii) the DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesthat implement, facilitate and provide supportive and ancillary serviceswithin the rubric of the competitive and collaborative capabilities, afoundational aspect of DTS wherein different Outcome Models may becompared systematically, by inspection and by internal and externalprocesses; iii) in certain modalities in the execution of some types ofAssertional Simulation wherein elements within a Reference Data Modelmay be apportioned in percentages (e.g., a non-zero-sum game) butevaluated against alternate apportionments in the context of acompetition (as may be advocated by other users as in ii) above and/oras part of a looped optimization operation) where the selection of the“best” apportionment constitutes a zero-sum game; iv) in user- andsystem-accessible tools and user interfaces which, in embodiments, arefunctionally framed and labeled as “strategies and tactics” (reflectingcertain game-theoretic models and principles) and constituted withincertain modalities of competitive Assertional Simulation wherein usersemploy these tools to engage in both zero-sum and non-zero-sumcompetitions (for resources in an organization, for example or to securethe winning bid for a proposal).

In another example, suppose DTS Assertional Simulation is applied to aproject management challenge. Here a DTS user wishes to simulate variousorganizational configurations, deploying Assertional Simulation to testand evaluate the efficacy of different proposed organizational schematato be used to manage the deployment of, for example, volunteers in acharity event, and ultimately, to assist in calculating the respectiveresource costs and to assess efficiency under different deploymentscenarios. By using various user interfaces available within the DTSplatform, this DTS user may construct a variety of different personnelallocation schemas which may be centered, for example, around functionaltask-types, creating departments to which volunteers may be assigned,such as food vending or seating management. The result of creating theseproposed departments and specifying personnel assignments may berendered using Assertional Simulation and inspected through DTS-supplied(or DTS-enabled) visualization techniques. Applying a simple butcommonly employed extension, assume further that the DTS user wishes toalso allocate the time each volunteer should spend in each assigneddepartment, so that a given volunteer may be assigned, for example, towork in one department 60% of the time and 40% of the time in another.In more sophisticated variations, but one that is not uncommon incertain applications, Assertional Simulation may be integrated withiterative or looped optimization techniques where the definition ofoptimal Outcome Models may be specified by the user (or by the system),and result-sets created by repeated simulations wherein modificationsare made to simulation parameters, and the resulting Outcome Modelscompared to the synthetic ideals until a best-fit parameter group isfound.

In this example, the DTS user possesses a point of view regarding themost efficacious deployment of volunteers within various departments andthe best apportionment of their time, and, using DTS-based interfacesand procedures, parameterizes this arrangement. This user may then view,inspect and examine the DTS result-sets that reflect this proposedorganizational schema and apportionment plan through graphicalvisualizations and analytic renderings. In this sense, as mentioned inthe foregoing, the result-set itself constitutes the analytic productand the graphical rendering is simply a method to view the data. TheAssertional Simulation results may be examined, inspected anddeconstructed and compared manually and visually without the need foradditional computational processing.

As a reflection of the competitive aspect of DTS, suppose further,however, that the DTS user serves as part of a committee that has beenassigned to organize this event and that each member is also a DTS user.Each user could then construct their own view of the best plan forvolunteer deployment and time apportionment, noting that each suchresult-set reflects not only a user's point of view but embedded biasesand predispositions, as well. One committee member, for example, mayview a certain volunteer as being particularly well-suited to a certaintype of job in a certain department, while another user sees thevolunteer as having skills. Even in cases where the previously-describedoptimization techniques are deployed, such algorithms generally requiresome initialization, input choices which may, in general, also reflectuser bias. In this example, the committee may use DTS-provided orDTS-enabled visualization interfaces (or in some cases, analytic tools)to examine by inspection the relative merits of the views of eachcommittee members and may then make choices concerning the best plan.

In another example, suppose a bibliophilic collector (“collector” or“bibliophile”) possesses a large and diverse collection of documents,books, journals and audio and video media (in various formats andencodings), a diverse assemblage traversing a variety of topics andgenres, but which includes both physical and virtual forms, such asbound books (paper and electronic), paper records, handwritten notes andfiles, newspaper clipping as well as e-books, web references and otherelectronic documents. This example (and others) will be cited throughoutthis disclosure and expanded in context as exemplary of the many aspectsof DTS and Assertional Simulation.

In the initial portion of this example, let the collector be concernedonly with a sub-section of his collection, bound books, and let theexample focus upon of N instances of bound books—that is, at present,the bibliophile will be applying DTS Assertional Simulation only to theportion of his collection consisting of printed and bound books. Supposefurther that the bibliophile wishes to organize this collectionsub-section according to subjective preferences and point of view—thatis, in a manner that comports with how the collector wishes to accessand utilize this resource, but which also reflects their view of theoptimal, most accurate and most efficacious representation of thetopical composition of each element within the sub-collection. In otherwords, the schema reflects the collector's perception of ground truth.Finally, this example assumes that these ground truth opinions are basedupon a high degree of familiarity with these books but also a particularset of predispositions and prejudices about the potential topicalorganization.

In this example of an application of Assertional Simulation, thebibliophile creates a list of the N titles (a data structure termed flatin DTS parlance). This list of book titles composes the DTS Target DataStructure. Based upon familiarity with the collection, the collectorthen constructs a categorization system composed of M categories which(initially) has the following rules: a) every title must be assigned toone of M categories; b) every book may be part of one and only category;and c) there are no sub-categories (and thus, this particular constructof categories is also flat in structure). Further, the collector assignsa binary inclusion factor to each title in the target list as viewedthrough the categorization structure, such that the binary inclusionfactor value 100 denotes that a title is part of a category and 0denotes that it is not.

But note that in this example, the collector permits a null designationby which he intends to convey that the so-designated title has no knowncategory; note, as well, however, that DTS (in general but not in allcases) considers this designation a computational nullity, and that thecategorization of null for that title would be undefined. Nullity willplay a minor role in this present example, but is not a rhetorical ordidactical conceit since, in variations, nullity provides operationalfunctionality in certain DTS operations, and though it may not apply inall variations of Assertional Simulation, it may be included in otherexamples within this disclosure.

In the context of this example, the target data comprises an unorderedset of N titles, a structure subsuming a set of N information nodes(though, other target data configurations and orderings are possible).Likewise, the categorization structure, another flat list of categoriesconceived by the collector represents the (current) perceived groundtruth or point of view about the proper organization of this set ofbooks, a schema the collector wishes to impose upon his collection. Inthis instance, each element in the categorization structure—that is, thecollector-proposed schema of M elements—also possesses an associated butnot-yet-assigned scaling metric used to category denote inclusion.

Using DTS user interfaces, the example bibliophile employs DTSAssertional Simulation to impose his categorization schema (the proposedassertion of a nominated version of ground truth regarding the best wayto topically classify the books) such that the structure of theassertion is, in this case, applied to each instance in the list oftitles (nodes within the target entity), coercively imposing each scaledelement from the parametrized schema upon each node within the target.Thus, each category and its assigned metric (in this case 0 or 100) areapplied to each title in the target list. But since, in this version ofthis example, a book may be relegated to one and only one category, theapplication of the scaled assertion upon each node results in a binaryassignment status for each book, so that, comporting with the statedrequirements, each title may be assigned to one category (and thus givena metric of 100 for this category) and is designated as not assigned toeach other category (and is thus given a metric of 0 for each of theseremaining categories). The result of this simple instance of AssertionalSimulation is a derivative informatic entity, in this case, a2-dimensional key-value structure.

This result exemplifies certain core aspects of DTS in a number of waysincluding without limitation: 1) the application of AssertionalSimulation to two 1-dimensional structures (that is, to two flat lists)produces a 2-dimensional outcome structure, an axiomatic mathematicalby-product of DTS projection which is usefully leveraged in otheraspects of DTS; and 2) the outcome, as with other DTS projectiveoperations, may be formatted for inspection in any number of ways,including for example, as a 2-column list of N rows of book titles whereeach such row references its category in an adjacent column. But thedata may also be organized for visualization and inspection in anynumber of other ways, including, (again, in a non-limiting example) in amatrix presentation wherein each category is arranged as a column headerand each row contains a title and is assigned either a moniker of 0 or100, as described.

This example and the use of Assertional Simulation by the bibliophile toimpose and refine a categorization system demonstrates one elementalmethod that DTS users may employ wherein Assertional Simulation maysystematically project a particular view of a truth upon an independentcollection of information. In this instance, the operational deploymentof DTS interface structures effectuates the projection of a structuredtopical schema upon a collection of books via a node-by-node DTS Shannonentropic imposition of the user-nominated structured schema upon thetarget collection, the net operation reflecting the actuation ofparticular point of view or a ground truth about the composition of thetarget data.

In embodiments, DTS embodies improvement and systematic integration andactualization of techniques from disparate bodies of art that in thepast have been applied piecemeal in business and professional situationsfeaturing changeable and emergent requirements and/or that tend toinvolve different points of view among involved individuals,stakeholders or other entities. Since the integrated solution-set of DTShas not been previously constituted, an architecture, framework, methodsand systems (collectively referred to as the System) are disclosed thatencompasses components from distinct domains, with contextualimprovement, refinement, enhancement and integration of such componentsand with unified operating principles that characterize DTS, itscomponents, and its variants.

To illustrate but one example of the contextual refinement of adomain-specific operation, the terms coercion and DTS informaticconvolution (and other forms of convolution) are used in this disclosurein connection with the application of an assertion to a targetstructure. These terms encompass meanings ascribed to them in computerscience and mathematics, but are also used here to encompass, exceptwhere the context indicates otherwise, a broader range oftransformations that may be applied, such as by applying one or morecomputations defined by an assertion, to one or more nodes in a targetdata structure. In embodiments, such as those involving bipartitediscrete projection, such computations may be applied to every node inthe target data structure. DTS projection does not always strictlyconvolve elements from the proposed schematic assertion with elementswithin the target structure, at least as convolve is commonly used insuch fields as statistics, signal processing and computer vision (where,tangentially but not arbitrarily, DTS also has application). In thoseand other fields, convolution typically refers to the result produced bythe application of a convolution operator between two parameterizedfunctions to produce a third function which is (in general) applied tothose parameters, such that this latter function composes a transformedversion combining (in a domain-specific fashion) the first two functionsand thus, upon application to the parameters, produces a new buthybrid-based result. While a DTS Assertional Simulation may use justsuch a convolution, as will be seen below, DTS projections may involveoperations that produce a derivative data structure based oncomputations on a target without strictly (or simply) applying aconvolution operator but rather may combine such unique operations withcontent-sensitive and content-responsive methods and procedures andinterfaces sensitive to system context and user intentionality—again, acomputational scenario referenced as DTS informatic convolution. Butnote that the term coercion typically refers in computer science to theexplicit and forced conversion of one data type to another, a commonoperation that is often also called casting. In implementations andvariations, DTS may involve such coercion or casting, such as at thecode level within various DTS components, but DTS employs this term inreferences that are relevant to and which encompass the novel DTS-basedre-referencing of the ontological underpinning of information in atarget data structure, a characteristic of DTS discussed and illustratedin the following paragraphs.

In a simple use example, suppose the manager of a section within aretail operation (a local hardware store, for instance) advocates amarkdown of certain items in his department as a type of loss leader,promoting his personal belief (which may be based on intuition orobservation or direct experience) that such a strategy would enhance notonly the profitability of his section but would provide opportunitiesfor other sections, as well. DTS Assertional Simulation provides bothtools and an operating environment by means of which users may createand manipulate unique analytic products that may be used to prove (ordisprove) the veracity of these types of assertions but also novel meansto manipulate, restructure and otherwise prepare such data its analyticproducts for application of other DTS-integrated and DTS-accessibleanalytic processes and techniques. The application of DTS andAssertional Simulation in such business and corporate environments willbe explored in the following paragraphs, expanding this and otherexamples to illustrate the unique capabilities provided by DTS and itsoperational component.

The DTS operating environment supports extensions to and enhancements ofthe information processing and analytic capabilities characteristic ofAssertional Simulation. In embodiments, DTS provides not only standardframework-like capabilities (such as API support and a variety ofstandard user interfaces) but additional capabilities, such as forexample, without limit, specialized user interfaces, services, features,and extensions providing the means to the create, reconstitute, modify,enhance and otherwise manipulate assertion and Reference Data Models andelements thereof, and to similarly modify and parameterize Apportionmentsub-Models; methods and procedures and interfaces that, in variations,enable DTS-based, DTS-enabled and system-external analytic computationalactivity of DTS models and elements thereof, of information that may becomposed within DTS models and other analytic activities.

One such facility within the DTS framework is called the DTSEntity-Management Fabric. This is a collection of methods, proceduresand interfaces that may be present throughout diverse segments of theDTS system. In some instances, the DTS Entity-Management Fabric may beinstantiated as standalone procedural blocks and in others as integratedinterface-accessible elements within other computational and proceduralblocks, or in some cases in both forms. In all cases, theEntity-Management Fabric facility provides services (or contributesservices) in support of the disposition and communication among andbetween three main entity-types in DTS: (i) users and groups of users;(ii) data elements and structures and groups and subsections thereof;and (iii) system methods and processes and groups and subsectionsthereof.

It is noteworthy that the methods and procedures composing the DTSenvironment and which support application of Assertional Simulation andits by-products interact with these main entities (users, data andprocesses) through the Entity-Management Fabric (E-MF) and in thissense, E-MF, in embodiments and variations, provides a type of interfacefor these managed entities. More relevant, however, is, that, asmentioned previously E-MF (and DTS) explicitly treats the entities asfunctionally but not necessarily compositionally equivalent. In manyDTS-based and DTS-enabled applications and functions, there is littlecomputational distinction between these entities, though there arerequired exceptions such as order of precedence and certain logicaloperations and conditional operations. Moreover, certain API's and userinterface methods must, to one degree or another, process entities ascompositionally distinct, and in some variations, there are otherinstances, but in general, the functional (and sometimes logical)equivalence of DTS entities (users, data and processes) is pervasive(though not absolute).

The nominal (and in many cases, actual) architectural andexecution-level equivalence in DTS between the logical representationsof these objectively distinct entities may be unusual in systems of thistype, and certain practical limitations driven by real-worldcircumstances may be appreciated in light of the disclosure.Nevertheless, the manner in which users, data and processes are managedby the DTS Entity-Management Fabric may be premised on this equivalence,keeping in mind the many context-based exceptions that arise infunctional practice. E-MF operates upon and in conjunction with theseentity-types, by, for example integrating methods similar for each type.In general (but without implying limitation) E-MF supportsinterconnection of inter-process information-exchange between nominallydisparate components, which, in variations, extends, enhances andfacilitates DTS-based functionality. In some cases, such integratedcontrol mechanisms may operate transparently to the user. In othercases, elements of E-MF may also be visible and explicit to systemusers, and internal and external processes through one or moreinterfaces and/or control mechanisms and semaphores, and in some cases,may additionally be subject to and/or may require user and/or systemcontrol and intervention.

There are at least three operational facets within the DTSEntity-Management Fabric, and while each address operations related tosystem entity-types, each facet addresses different operationalconsiderations, providing distinct but often intersecting andinter-operational functionality. It is noteworthy that while the currentdescription of the principles characterizes each as coexistent withinthe E-MF, each facet may be implemented without the other and mayoperate independently, and thus, in spite of the present pedagogicalexposition, this rhetorical convenience should not imply functionalinterdependence. Nevertheless, for convenience, the current descriptionpresents these parallel systems as coexistent. These facets areAssociational Process Management (APM), RAMS (Recombinant AccessMediation System) and Topical Capability Mediation (TCM).

The first facet of the DTS Entity-Management Fabric, RAMS (RecombinantAccess Mediation System), executes and arbitrates informational andprocess security with respect to permitted activity between entities(data elements, procedures and users), constituting layered andcustomizable associational control schemata. In embodiments, thiscollective and in some variations, distributed functionality is subsumedwithin an integrated and interconnected control system. Inimplementations, RAMS is, in general (but not in all variations)event-triggered, executing control and resolution over object- andattribute-based access control with respect to permitted activity andinformation sharing, mediating collections of access and correlationalparameters associated with system entities, where, in variations, theseparameters (and elements thereof) may be fungible, transient andcontent- and context-sensitive. In its most general expression, RAMSfacilitates, mediates and (occasionally) resolves disparate inter- andintra-entity association permissions, and provides a dynamic andadaptive computational fluidity woven within a customizable schema andcontrol fabric but nonetheless implemented in concrete interfaces andprotocols.

In implementations, RAMS-based control elements and embedded informationmay be composed within one or more dynamically constituted, dynamicallymaintained exchange units (such as certificates, in but one non-limitingexample, or, in variations, modulated within distributed (and sometimestime-displaced) encodings that may be embedded within, referenced to,attached to, integrated within or comprised (all or in part) within orcomprising one or more autonomous or semi-autonomous agents). Howeveractuated, such exchange units generally function as informatic vehiclesthat deliver or may otherwise make available context-responsive controldata within or between processes. Such context-responsive control is, ingeneral but not exclusively relevant to and between target elementswhich are, in general, but not in all variations generally reflective ofaccess and associational conditionals. In embodiments, suchcontext-responsive control may apply to and between the entitiesdirectly and indirectly involved in one or more RAMS-qualified orRAMS-controlled events, process or sequence thereof.

The synthesis, creation, modification, distribution and manipulation ofRAMS-based mediation and control information is, in general, but notexclusively, generated by or spawned in response to one or morecomputational events or groups of events or computational sequence orcollections of logical and/or compositional conditions. Both the contentand the delivery, and thus, the resultant mediation-result may, in partor in full, also be sensitive to, may be responsive to and may beadaptive in relation to some or any or all of the following non-limitinglist of factors, conditions and circumstances which may occur in anycombination and in any sequence and, in variations, at any time: (i)computational and execution-type and context; (ii) event and executionsequence; (iii) time or frequency factors (iv) compositional and contentfactors, which may, all or in part be independent of the foregoing or,which in some variations and in some contexts, may be conditionally orinexorably dependent on any or all of the foregoing; (v) the nature andcurrent disposition of the relevant entities; (vi) the history, contextor accumulated execution parameters associated with one or more subjectentities.

In some variations of these methods and procedures, RAMS-based controlexchange units and delivery techniques may be distributed or madeavailable to subject entities by means of a variety of mechanisms orcombination of mechanisms, including but not limited to: (i) globalbroadcast to subscribing (and registered) target entities where suchbroadcasts may, for example, emanate directly or indirectly from one ormore centralized resources (where such resources may store, index,reference or contextually gather or harvest) the relevant informationand deliver it on-demand or periodically according to a schedule or, invariations, based upon a control signal or one or more conditionsoriginating from some other (that is, external) RAMS or non-RAMSresource; (ii) peer-based broadcast wherein entities directly (or insome instances, indirectly) exchange control elements (as in theforegoing) through peer-oriented interfaces; (iii) agent-baseddistribution, where any or all of the foregoing may be co-residentwithin a system and/or may, all or in part, be distributed across one ormore sections within a network or other type of resource interconnectionscheme or, in certain variations, within one or more mesh-likecommunication structures or networked geometries; (iv) ad-hocassociational schemes wherein control elements (as in the foregoing) maybe distributed from a local but possibly transiently center whereassociated groups may or may not always be physically or logically orcompositionally local but which affiliate according to any of all of theforegoing criteria.

Thus, RAMS supports, manages, maintains, executes and otherwise supportsdynamic event-sensitive, event-triggered adaptive interposition,mediation and resolution of access, affiliation and execution rightsbetween qualified entities and groups of entities, where such controlmay be distributed in a variety of methods, as in the foregoing, through(possibly dynamically spawned) combinations of global, local andpeer-based techniques. RAMS-based exchange units may subsume orotherwise incorporate not only functional permission-related,security-related and conditionally-sensitive access, affiliation andexecution information, such as, for example, entity-related access,rule-sets and functional execution parameters, but, in variations,cryptographic-based protections (in the form of secure signatures, forexample).

The second facet within the DTS Entity-Management Fabric is calledAssociational Process Management (APM). As in the foregoing, APM may, inembodiments, co-exist with RAMS within the DTS Entity-Management Fabric,but, without loss of any functionality, both RAMS and APM (and elementsthereof) may, in some implementations, operate independently, and may,in variations, operate in tandem conditionally in response to one ormore internal or external conditions. Thus, RAMS and APM may, in someimplementations, conditionally execute either mutual or one-wayinteroperability, as well as fully independent operation, functionaldispositions that may, in variations, emerge at any time in response toany internal or external condition.

Associational Process Management (APM), like RAMS, centers aboutprovisioning services that support operation, inter-operation andinformational exchange and other types of interface involving Systementities; in contrast to RAMS, however, APM primarily actuates,administers and manages the cross-binding or associational linkagebetween entities, entity-types and groups of entities, entity-types andbetween elements composed within such types and groups thereof. Note,therefore, that as mentioned, in some variations, both RAMS and APM mayshare functionality, but that in other variations, interoperability (andinter-dependence) and shared methods and interfaces may not exist.

Nevertheless, in implementations, there may be not only interoperabilityand operational overlap between RAMS and APM, in some variations, theremay also be shared methods and procedures, especially in variations thatdeploy agent-based information delivery units within RAMS functionality.In most operational contexts, however, the lines defining the respectiveexecutable spheres are more pronounced: RAMS actively and dynamically(and in variations, on an event-motivated basis) delivers services thatenforce (or release) numerous access-related, affiliation-related andinformation-sharing constraints, conditional and context-based andcompositionally-based exceptions and contingencies, qualities thatinclude, for example, without limit, permissible computationaloperations and sub-operations involving specific entities, allowablelocal and non-system entity associations, and operational andidentity-based controls exerted upon input-output functionality. APM, incontrast, provides broadly-based, indexing and associational servicesthat are, in general (though not in all variations and contexts)process-oriented, providing services that manage, for example andwithout limit, the creation, filtering, parametrization, augmentation,maintenance and permutation of DTS (and DTS-enabled) identity andidentities, where such identities may be attached to, indexed to,associated with, entities and groups of entities, directly or indirectlyand transiently or permanently.

Thus, as a component within the entity-management fabric, AssociationalProcess Management (APM) in general, provides system-wide services that,in implementations, may be used by and within DTS methods and procedures(as well as by other non-DTS systems). In this sense, APM is similar toRAMS in that the methods and procedures and interfaces and the means andmethods of its communication and information distribution and managementmay be fungible, and any or all of the foregoing descriptions associatedwith RAMS may in variations also be applied to APM.

Note, however, that in some embodiments, APM identity-centric andassociational services, as reflected in user- and system-initiatedconditions and configurations, provide one basis for execution ofextended capabilities and features offered in conjunction with the basicoperation of Assertional Simulation operations. By means of bothcentralized and distributed methods, procedures and interfaces, DTSentities (user, data and processes) may receive, be assigned or may beassociated with one or more DTS-specific and system-based identitieswhich are typically (but not exclusively) signified by or within one ormore indices and/or encoded blocks which, in variations, may beoptionally encoded, encrypted or otherwise obfuscated. Note, however,that APM indexing differs from, and in certain variations, complementslogical address-based indexing and designations, such as thoseadministered by an operating system or within pointer-based programs,where the latter typically references memory location or disposition ofthat entity or logical block.

Thus, in the operational context of Assertional Simulation, APM spawnsand manages these (optionally encoded) indices while also dynamicallymaintaining the associational schema binding and relating such entitieswithin (and in variations, outside) the DTS system. In general, but notexclusively, APM implements two or more operational or executableaspects: (i) the creation and management of one or more indices tied toand which defines an entity; and (ii) the creation and management of theassociation between entities and groups of entities as referenced bythis index. Inter-entity association can, in implementations, be relatedto the existence (and in some instances, the maintenance) of theidentity-conveying indices, and thus, these two properties may, invariations, have a bilateral functional dependency. If, for example, theindex of an entity is changed or mutated, the association skeinreflecting logical bindings attached to and with that entity may alsochange. Further, if the entities composing an associational skein changeor mutate, so too may one or more of the indices referencing theseelements. It is nonetheless also true that in implementations, suchdependent mutation may be conditional or optional.

In any case, the collection of methods and procedures and interfacesthat compose and execute the associational properties provided by APMmay, in variations, integrate broad and flexible functionality by meansof which the fabric may administer, manage, maintain and otherwiseprovide executional coherence to the dynamic, context-sensitiveownership properties within DTS, wherein one or more entities may beowned by, or may be bound to or may be associated with one or more otherentities. Note, however, that DTS-based associational properties subsumebroader functionality than, as one example, the direct and simplebinding of a user to a data store, and the additional breadth of thefunctionality provided by APM components may, in variations of DTS,provide one basis for operational and functional extensions of DTS andAssertional Simulation.

APM-administered identities assigned to system entities may, invariations assume a number of forms, and these modalities (and methodsattached to such modalities) may, in variations, be fungible and/ormutable in respect to computational context and entity-relatedproperties, including but not limited to the composition,execution-based and nominal functionality, history, past and potentialaffiliations and logical, physical and geometric location anddisposition of the entity. In implementations, such changeability may beconditional and/or transitory or permanent and may be based on variousexecution- and content-related conditions, including but not limited to:(i) one or more computational- and execution-state conditionals andcontext; (ii) properties inhered in or associated with one or moreconstituent elements within such computational and execution statecontexts, where such subordinate (or constituent) properties may, invariations, be derived from or may be based upon or may be inferred fromone or more aspects of the content contained within or referenced by orotherwise associated with the entity or by the geometric arrangement ofthat entity or based upon one or more informational elements that may beassociated by reference, directly or indirectly with that entity. Invariations, combinations of the foregoing conditions may be present butmay, as in the foregoing be conditionally or transiently applied.

In implementations, properties that distinguish DTS-spawned entityidentifiers may include (but may not in all variations be limited to)entities that may be: (i) singular but which possess a uniqueidentity-type, such that this entity may not be grouped with any otherentities (where examples of this exclusive singularity may includeuser-types unique within the system, such as an executive-level systemadministrator); (ii) singular, as in the foregoing, and which thuspossesses a unique identity, but which in contrast, may, in someconditions and in variations, be aggregated with one or more otherentities (of any type) in groupings but such that, when so-aggregated,may retain both its individual singular identity as well as designationsconveying inclusion in its associated aggregations; (iii) generic ortype wherein the entity may share a designated DTS identity with otherentities but, as in the foregoing, where any such entity may, invariations, nonetheless also possess (or be assigned) other propertieswhich confer a supplemental identity, including singularity as in (i)and (ii) in the foregoing, where such generic entities, in variations,may also optionally be additionally aggregated in other groupings and,as in the foregoing, when so-grouped, may optionally possess both itsgeneric identity as well as designations conveying inclusion in one ormore of the associated aggregations.

In some embodiments, combinations and permutations of the foregoingproperties of these DTS entity identifiers may be present and, invariations, such options may be both flexible and adaptive so that anyof the described properties and operational modalities may, for example,and without limit, be alternately static or contextually dynamic oreither transiently or permanently fixed. In variations of theseembodiments, such combinations and permutations may be responsive tocharacteristics as the composition, structure, content, domain orencoding type of the subject entity and/or associated entities.

The pervasive, flexible and dynamic inter-entity associational functionscomposed within APM provide one basis for additional features andfunctionality within the DTS platform, capabilities that enhance andextend Assertional Simulation. First and most generally, these servicesenhance operational functionality surrounding creation and manipulationof the elements composing the elements of Assertional Simulation,including results within Outcome Models elements composed within orreferenced to such structures and which may contribute to theDTS-computational chain and to associated ancillary information. Thecapabilities provided by the identity management aspect of APM may beseen in many operational contexts, and are reflected throughout DTSimplementations, but may be clearly seen in the services related tocreation and manipulation of Assertion Models, Apportionment sub-Modelsand Reference Data Models.

Secondly, APM identity management services, in general but notexclusively, enhance and extend and, in variations, enable features andfunctionality associated with many DTS services in general and withAssertional Simulation in particular, functional enrichment thatmanifests in both DTS-based and DTS-enabled analytic and computationalfunctionality. These APM methods and procedures and interfaces (asdescribed previously but not in all cases limited to earlierdescriptions) includes (but may not be limited to): (i) indexingcapabilities that enable user- and system-driven experimentaldevelopment and comparison of user- or system-composed results ofAssertional Simulations (so-called what-if functionality), where suchcomparisons may utilize manual, automated and/or semi-automatedmechanisms, techniques and interfaces; (ii) indexing capabilities thatenable and enhance application of optimization techniques which mayinclude, without limitation, evolutionary (and/or so-called geneticoptimization), permutation-based and/or otherexperimentation-by-modulation-based procedures, wherein interim orintermediate or final results may be separately or associationallyindexed and preserved, encoded or otherwise logged and which may also orinstead be compared to and/or correlated against one or more similar orcorrelated or deliberately structured optimizations, prototypes,archetypes or intermediate or past results, where such comparisons mayalso or instead utilize manual, automated and/or semi-automatedmechanisms, techniques and interfaces and inspection methods; (iii)indexing services that enhance or extend the application ofpermutational and transformational (as well as non-modifying)statistical and probabilistic computations and procedures which, invariations, may be executed in the context of any of the foregoing (andas may be applied using any user- or system-initiated procedure or inthe context of any arbitrary aggregation of entities, also as describedin the foregoing) in order, in a limiting example, to create, transform,mutate, manipulate or otherwise change entities that (in a non-limitingexample) may embody forward-looking or backward-looking projectionsbased upon such example computations as rolling averages, linearregression and other projective computations, where such synthesizedobjects comprise statistically-derived results based upon (or extractedfrom) one or more results typically embodied within one or more actualor synthetic entities (such as, for example, one or more Outcome Models)that may, in variations and in some contexts be arranged to compose oneor more points in one or more time- or event-based progressions, andwhere (in another non-limiting example variation) such progressions ofOutcome Models may be separately or associationally indexed and whichmay result from one or more permutations applied to the content orstructure within one or more of the progenitorial models composing thisprogression of Outcome Models, and such that these permuted progressions(or any elements composing the progression or composed with such aprogression) may be compared against other progressions using manual,automated and/or semi-automated mechanisms, techniques and interfacesand inspection techniques, and such that (in a non-limiting example)additional statistical and computational methods may be applied to theseresults (where these results may also be indexed), and/or where suchresults may be used within one or more overarching optimizationprocedures, as described in the foregoing, and wherein one or moreelements within any of the entities may be systematically or arbitrarilymodified, also as in the foregoing; (iv) indexing services thatassociate and correlate such ancillary and supplementary data andinformation with the procedures and processes from which they may bespawned, including but not limited to: computational by-products, systemand user logs, and other information produced in the context of any ofthe foregoing, such that any or all such information may be accessed bymethods and procedures and through DTS user interfaces.

Third, APM-based associational and indexing functionality (optionallyexecuted in conjunction with using any or all of the methods, proceduresand mechanisms described in the foregoing and with other methods andprocedures that may not be explicitly mentioned but which may beappreciated in light of the disclosure), provides the means and basisfor the extension of Assertional Simulation within the DTS-basedcollaborative and competitive environment. In implementations, DTS-basedcollaborative and competitive methods, procedures and interfaces enableone or more users (and, in variations, adjunctively with one or moresystem processes) to submit one or more entities (which are, in general,but not in all variations or instances, results of AssertionalSimulation) to system-enabled competitive and collaborative comparativefunctionality which may include (but may not, in cases and in allimplementations, be limited to): (i) selection or assignment of one ormore ranking metrics to one or more members of a collection of suchresults, where the objective in such scenarios may include (but againmay not be limited to) designation of a single winning Outcome Model,group of Outcome Models, or one or more time- or event-basedprogressions of Outcome Models, where such designation of a singlewinner may be typical in zero-sum game scenarios and such that thesewinning Outcome Models may be uniquely indexed as such; (ii) assignmentof one or more ranking metrics to any of the foregoing whereby one ormore Outcome Models (or groups of Outcome Models) may be arranged orordered by ranking but also such that there is not a single winner butrelative winners (as embodied within the ranking order) as may betypical in non-zero sum games, but such that, in variations, more thanone ranking metric may be applied to determine the ordering, where suchmetrics may be weighted with a possible result that the Outcome Models(or groups of Outcome Models) may be ordered differently depending onmetrics employed or the weighting applied, and such that each so-rankedOutcome Model may be uniquely indexed both contextually and in absoluteranking terms; (iii) alternative implementations of the foregoingwherein any or all of the foregoing normative techniques may be combinedwith these and other competitive algorithms where such choices may bemade by user and/or system processes in pursuit of a variety ofobjectives, including continuing optimization and re-evaluation, andsuch that any or all indexing and associational functionality may alsobe applied, as in the foregoing.

Finally, as mentioned in the foregoing description of DTS entityownership and inheritance, in embodiments, any or all of the informationindexed to, related to or derived from and/or as may be associated withany APM-administered entity (or group of entities) may be inherited fromor may be otherwise acquired from one or more other entities. Suchinheritable or assignable properties may be optionally modulated orrevised by users and/or system processes (subject to system as well asRAMS and TCM constraints) including those that may be used and modulatedin various RAMS and TCM mediation functions. In general, therefore, inthe absence of exceptions that may arise in embodiments and variations,and absent one or more conditional exemptions that, in contexts, mayconditionally apply and/or may be conditionally applied by entities orby DTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces within a Platform 1000, DTS ownership andinheritance properties apply to any or all information aggregates thatmay be associated with an entity or groups of entities including (butlimited to): i) one or more elements within or related or as may bederived from one or more associational and execution capabilitiesadministered by RAMS and TCM; ii) ownership, cross-ownership and otherdescendant and inter-entity relations that may exist; iii) DTS entity“ancillary data” (properties and characteristics); iii) entity-centricor system-related ancillary information generated by or derived from anyof the foregoing functionality (and from variations thereof); iv)entity-centric or system-related generated information that may begenerated by or may be derived from the administration of RAMS and TCMcapabilities; v) entity-centric or system-related generated informationthat may be generated by or derived from any DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures andinterfaces; vi) entity-centric or system-related generated informationthat may be generated by or may be derived from non-DTS sources as maybe obtained or accessed entities or other facilities within a Platform1000.

Thus, the DTS-based (and/or DTS-enabled and/or DTS-accessible) methodsand procedures and interfaces within or associated with APM manage theownership skein so that when an entity owns or acquires ownership ofanother entity, however that relationship may be actuated, including,for example, explicit assignment of ownership of an entity (includingitself) by an entity to another entity (where an exemplary case herewould be when a user assigns ownership of an aggregation of informationto another user), the “owned” entity assumes and is assigned (or“inherits”) all of the foregoing information assigned to the entitywhich acquired it, though, as noted, in embodiments, certain informationmay be exempted from inheritance, and some may be optionally elided (bythe system and/or by the user) from the inherited information. In theevent that the “acquired” entity already possesses some or allinformation in the foregoing, DTS-based (and/or DTS-enabled and/orDTS-accessible) content-sensitive and content-responsive methods andprocedures and interfaces within or associated with APM may execute anumber of reconciliation options, including but not limited to: i)harmonization of one or more elements of the “new” information by, forexample, combining or synthesizing certain information aggregates; ii)and/or eliminating one or more elements either from the “new”information or from the existing information to avoid contradiction;iii) and/or annexing or incorporating or otherwise including the newinformation. Note that in any of the foregoing options, APM may index,annotate, log and otherwise mark the “new” information and/or maygenerate (and optionally attach or reference) information about the“new” information, including the conditions, context and other detailsrelated to or surrounding the acquisition or assignment of theinformation.

Note, however, that since the APM ownership inheritance capability alsoimputes the connection between entities from an owner to an owner oracquired entity, the entity-centric, network-based affiliations of theseownership-threads may spawn and permute new relationships. These newconnections may grow and permute combinatorically as different aspectsof the Assertional Simulation execution chain (and associated methods)operate upon new and existing entities and generate additionalinformation about such entities. Such interconnections may beincorporated in and used as input variables by a variety of DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces that execute analytic, pattern detection, feature mapping andother AI-based routines and other advanced computational facilitieswhich, in some applications, may provide unique analytic informationthat may be utilized by users but may be incorporated within DTS in-Appactivity metrics.

This aspect of APM reveals an extension of one feature of AssertionalSimulation mentioned previously: that the Outcome Models and theassociated progenitorial models and ancillary information themselves notonly constitute a standalone analytic result, but that the additionalcapabilities provided by APM to restructure and scale information may beleveraged to prepare, format, restructure or precondition informationfor presentation to other analytic routines. But APM provides a deeperand more unique capability, as well: since APM optionally indexes allthe entities that may directly or by association contribute to thecreation of any element in the Assertional Simulation model-creationchain (including, for example, intermediate and transient entities), andsince many other entities may be, or at one time or another may havebeen affiliated with a given or related Assertional Simulationmodel-creation chain, the breadth, depth and richness of informationthat can be made available to analytic computations is both extensiveand unique. This system-wide preservation (even in encoded form) of thewidely varied information spawned throughout the various executionchains invoked in modalities of Assertional Simulation (and relatedancillary and supplemental operations) illustrates another instancewherein methods and procedures and interfaces within (or accessible to)a DTS Platform 1000 may increase the Shannon entropic richness of theinformation within a DTS Platform 1000 and may make that informationavailable to both DT-based and non-DTS analytic components.

The previously cited project management example may be expanded toillustrate some of these capabilities and highlight the pivotal andenabling potentials provided by DTS APM in common businesscircumstances. Recall in this example that a committee of DTS users usesAssertional Simulation and tools within DTS to evaluate the relativeefficacy of different organizational schemas to manage the deployment ofvolunteers in an event.

But suppose that after one round of simulations in which each membersubmits their outcomes, the committee is not satisfied with the results,and the members instead agree to not only split into groups and creategroup Outcome Models, but that each such group would run as manyinstances as they wish. This is a common type of mode of activity inmany settings in which groups of individuals construct competing visionsof the best course of action. DTS provide the means to formalize whichAssertional Simulation and associated and ancillary processes, asdescribed in the foregoing, provide methods and procedures andinterfaces by means of which users may engage in such commoncompetitive, game-theoretic exercises. But note that, as describedthroughout this disclosure, the novel combinations of methods andprocedures and interfaces that constitute embodiments of DTS enhance andextend these prior capabilities.

In the current context of this example, note that as described in theforegoing, methods and procedures and interfaces within the APM fabricmanage, annotate, index and otherwise administer not only the individualidentities of each member but both the (presumably temporary)association-spawned identity of the group as well as its composition.Moreover, as described, as each group constructs, edits, modifies andrefines its Assertional-Apportion Model pairs, APM manages the indexingof each these and associated objects. Note further that the recombinantnature of the RAMS permission and access mediation system describedelsewhere herein accommodates these transient associations, and itsmethods and procedures and interfaces interpose or relax restrictions inthe event and to the degree security issues may emerge in thesecontexts. Finally, each user and each groups of users “owns” andcontrols all of the information created by their separate operations andmay use specific user interfaces to pursue strategies to gain advantagein the position of their assertions over others. One such strategy iscalled “selective reveal” wherein the owner of an informatic structureleverages the RAMS capabilities that proceed from that ownership toregulate who may view (or act upon) any part of such “owned”information, where “selective” may mean that only designated parts ofthe information are revealed to one or more users who otherwise have nosuch rights and/or it may mean that selected users may see suchdesignated information. The selective reveal strategy is but one exampleof the game-theoretic and strategy-based functionality that inembodiments pervades DTS, wherein the broad and unique functionalityprovided by APM and RAMS enables a systematic management of commonactivities in new and novel ways that extend and enhance system and usercapabilities.

In the context of this example, it may be seen how DTS tools may beemployed to enable, enrich and facilitate the process of creatingeffective Outcome Models. As but one example of the deployment of theseenhancement capabilities (as cited in the foregoing), users may employ avariety of iterative optimization routines (which may either beintegrated with or may be made available to DTS) where such routinesmay, in variations, be configured in a variety of ways, including (butnot limited to): (i) fully automated optimization looping in which anOutcome Model would be compared to or analyzed against some set ofparameters, and such that Assertion-Apportionment Model parameters wouldbe adjusted until some optimization criteria is met; (ii) one or moreuser-assisted iterative automation routines looped iteration is employedas in the foregoing but such that there may be a greater degree ofparametrization and in-process adjustment; (iii) variations of theforegoing wherein both DTS-based and DTS-enabled statistical or othernormative analytic techniques may be applied to achieve optimal results.

In these contexts, APM may, in implementations, be configured toannotate and manage the associational indexing of any or all relevantparameters which animate, and which may proceed from such optimizationloops, including user and group-generated notes, logs and time-stamps.Note that in embodiments, such information is retained and factored intothe comprehensive suite of in-app metrics by means of which methods andprocedures and interfaces may not only provide feedback and otherinformation to users and system-operators but which may also provideparameterization (and additional variables) to the continuing evolutionof the content-sensitive and content-responsive methods and proceduresand interfaces used throughout the platform, capabilities described insubsequent paragraphs. In most instances and in embodiments, suchdetailed record-keeping may not always be invoked but APM nonethelessminimally preserves the associational aspects of the Outcome Models andtheir precursors, regardless of their etiology. Thus, even in lesscomplex, inspection-based, human-only optimization—wherein, in thepresent example, committee members would simply inspect each OutcomeModel and visually analyze the results—the associational and indexingcapabilities provided within APM provides the means to preserveinformation related to the provenance, ownership association andprogenitors and ancillary data of each potential deployment schemagenerated by the committee members.

Continuing the current example, in general, a winning Outcome Modelwould be chosen and thus, the event committee would examine the variousorganizational and deployment allocation submissions and through someagreed upon mechanism, would select a winning formulation (called theCardinal Outcome Model). In DTS terms, the creation and comparison and,ultimately, the selection of a single winner reflect the use ofDTS-fostered zero-sum competition of Assertional Simulation OutcomeModels, wherein the set of Models that produce this Outcome Model arecollectively referred to as the Cardinal Models. Note, however, that DTSalso supports many other end-point types of game-theoretic andcompetitive scenarios, such as, for example, non-zero-sum competitiveanalysis wherein relative winners would rank from groups of variousOutcome Models according to mutually agreed upon criteria.

There are many ways in which DTS users (and system processes) may selectCardinal Outcome Models, but in the context of the current example,suppose, the event organizers have a subjective belief that the sameevent was better organized two years ago than three years ago or lastyear—that is, they have a non-evidenced-based point of view of a certainground truth—but that they either cannot articulate why this is true(exactly) or, as a group, cannot agree upon the reasons. But supposefurther that the data is available for all 3 years and that this datacontains the implicit information that the year in question (two yearsago) incorporated a particular proposal model/apportionment modelstructure that was different from both the other years. Note, of course,that this example illustrates one reason that DTS users invoke theinformation-rich APM indexing and associational capabilities, asdescribed.

Thus, continuing this example, suppose that the APM-managed notes, logsand other information preserved via APM from two years prior (believedto be the best year), reveal that within those Cardinal Outcome Models,an experienced volunteer supervisor was designated for every 10volunteers and that for every 5 of these supervisors, a company-employedleader was assigned. On inspection, the committee discovers that thisschema was not present in either of the Cardinal Outcome Models of theother (less successful) years. Thus, the members decide to use thisorganization scheme.

But suppose instead that the facts above are not evident by inspection.That is, suppose that there was consensus that the subject CardinalOutcome Model from 2 years previous was indeed best but that there isnot enough human-readable detail in the APM-indexed information toreveal the reasons why this scheme worked so well. Thus, the eventorganizers would like to replicate the most successful approach but donot know how to do so because they cannot pinpoint (or agree) what madethat event successful.

In this example, users (and system processes) may invoke a combinationof APM-enabled indexing and associational capabilities in combinationtogether with DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces that permit users to prepareAssertional Simulation data for a wide range of analysis using a varietyof techniques which, in embodiments may be available within oraccessible to a DTS platform. Such preparatory capabilities may include,for example, without limit, invocation of any or all DTS-based (and/orDTS-enabled and/or DT S-accessible) content-sensitive andcontent-responsive methods and procedures and interfaces that may beemployed to dynamically restructure and/or reformat and/or recompose therelevant information (as described in the foregoing and as furtherexpanded in subsequent paragraphs). Thus, in this example, users mayselect a number of Outcome Models from a number of years and may includeall or part of the set of progenitorial models within (or related to)their genetic chains as well as all or part of any related informaticelements associated with their creation and evolution, and, asdescribed, process such information for presentation to and applicationof DTS-integrated or DTS-enabled analytic tools (including, for example,some combination of user-inspection, pattern recognition-based programs,statistical analysis packages and even external metadata) to discoverthe hidden characteristics embedded within the agreed-upon optimalOutcome Model. In this case, the happy result is that the analytictechniques surfaced the previously hidden information about the optimalorganization as described previously.

Note that those skilled in the art may see how the activities withinthis example may be extended to also include cost andresource-deployment factors where, in non-limiting examples, such datacould be extracted from one or more accounting systems, time managementsystems, user and customer survey results and which may then becorrelated with one or more organizational results, and may even includeother related data such as previous-year supervisor notes andcommunications (which may be evaluated by users with DTS-provided“intangible measures” and/or with normative results from naturallanguage processing and/or from correlative methods applied to keywordsearch) as well as spreadsheets that might provide such seeminglyunrelated detail as decoration costs and counts of cups and napkins. Insome instances, the selection of the optimal organization could bechosen by consensus derived as described or by automated methods or somecombination wherein the agreed-upon “ground truth” could be objectivelyverified through actual evidence using the novel extended capabilitiesof DTS APM, the platform competitive environment and the many methodsavailable to prepare and execute analysis of all types and complexity.

While the volunteer example demonstrates the deployment and applicationof certain novel capabilities within embodiments of DTS and as providedby modalities of Assertional Simulation, the activities described inthat example (and others) are extremely common in business and even insocial and non-professional environments, and the various combinationsof DTS-based (and/or DTS-enabled and/or DT S-accessible) interfaces,methods and procedures associated with and which support and enhanceAssertional Simulation (and ancillary operations) provide a wide rangeof unique capabilities to assist in these and other activities. Inparticular, this example reveals that the methods, procedures andinterfaces comprising the co-existing APM and RAMS control rubricsprovide a broad and enabling substrate upon which common transactionalactivity may take place and, with the plethora tools and interfacesavailable to users within DTS, these activities may be extended andenhanced. The sort of internal deliberation that is evident in thisexample is a common occurrence in organizations across the professionalspectrum: managers compete for resources but also advocate particularviews of ground truth and put forward opinions about how the interestsof the organization (or their part of it) may be pushed forward byselecting one proposal or another.

The role of user-to-data (as well as data-to-data indexing andprocess-to-user indexing and process to data-indexing) and the seminalrole APM plays in practical usage of both simple and advancedfunctionality in implementations of DTS may also be demonstrated byreturning to and extending the previously cited book collection example.First, recall, as described, the bibliophile has run the described DTSsimulation to apply a categorization system to the bound book portion ofhis mixed media collection with the results as previously presented.Now, let a nominally satisfactory instance of this simulation (asactualized within the coincident Outcome Model) be labeled by thebibliophile Book Cat v1 (shorthand for “book categorization version 1”).In implementations, a user would be able, through various DTSinterfaces, to assign and edit this type of name for whatever reason. Aswithin all such DTS scenarios, the specific Outcome Model referenced byBook Cat v1 is uniquely defined for the particular set of categories and(in this case) the binary (or null) assignments embedded within aparticular Assertion-Apportionment Model pair as these models had beenapplied to a specific set of books comprising that particular ReferenceData Model. It will be appreciated in light of the disclosure that achange in any of the progenitorial elements may result in a differentOutcome Model, one different than that referenced as Book Cat v1.(Noting, of course, the previously-cited exception (invoked in thisexample for pedagogical purposes) wherein null values within theApportionment sub-Model assigned to newly inserted Assertion Modelelements render the Assertion-Apportionment Model pair unchanged.)

In the present example, however, suppose that, upon reflection, thebibliophile decides to add new categories to his declaration (theAssertion Model), a decision indicating, for example a change in hissubjective view of the fidelity of his first attempt to represent thetopical variegation within his collection (as embodied within theOutcome Model Book Cat v1). The bibliophile may copy and edit the copyof the original Assertion Model, Assertion-Apportionment Model pair andthe like.

In DTS terms, this alternate view of ground truth would entail changesin either the Assertion Model or associated Apportionment sub-Model orin both. Note that in these instances, where a user may wish to makechanges to already-composed Models, DTS methods and interfaces may, inimplementations, optionally permit a user to retain the originalAssertion-Apportionment Model pair as constituted, thus preserving notonly an operating system-spawned logical index of the execution instancethereof, but other DTS-created associations and other informationpossibly assigned to that instance, while also permitting the user tomake the desired changes in a duplicated and newly indexed version.Methods and procedures within the APM identity fabric implement thesetypes of associational preservations. This example exemplifies what inimplementations is a commonly deployed DTS feature sometimes referencedas a clone and modify operation. In variations, there may be similar butpossibly more complicated cross-model instances of these operations. Anumber of variations and enhancements of these capabilities areavailable within other Assertional Simulation operations, and in someembodiments and in other contexts, DTS entity permutation processesenable functional enhancements that may be applied to AssertionalSimulation and its results and byproducts.

In the context of the current example, suppose the bibliophiliccollector decides to retain the results of the first simulation (BookCat v1) but, using one or more aspects of the DTS clone and modifyfacility, creates a duplicate copy of the originalAssertion-Apportionment Model pair but decides to add 3 new categoriesto this new model set. (While the current example cites addition of newelements, in variations, any number of other modifications may be made,including, but not limited to deletion, filtering and reorganization ofthe structure of elements). Let the entries within the ApportionmentModel corresponding to the new categories in this duplicated Model beassigned a null value, while the other values remain the same. Let theresulting Outcome Model be called Book Cat v2. Thus, given with theprevious stipulation regarding nullity, from the DTS standpoint, theresult-set, Book Cat v2 (and the Models composing its computationalchain) is functionally identical to the previous Outcome Model (Book Catv1) despite the presence of additional categories, again, since, asstipulated in this (non-limiting) example, a nullity attached to a titleas a category value entails that the associated title has no category.

Despite this functional equivalence with the previously renderedSimulation product (Outcome Model Book Cat v2), this new version withadditional but “null-valued” categories (Outcome Model Book Cat v2) andthe associated progenitorial models constitute a new instance withrespect to the Entity-Management Fabric and, in variations, one or moreAPM methods and procedures within that control skein may assign newlygenerated indices, not simply to the models but, in variations, to allthe information that may be, may have been (or may have become)associated with those entities, which, in other variations and incertain operational contexts, may have been duplicated, as well, inaddition to such ancillary information that may have been generatedduring the genesis of this new instance. But as with the operations theuser engaged to effectuate the clone and modify operations for OutcomeModel Book Cat v1, APM methods, procedures and interfaces also permitand facilitate the modification, augmentation and even deletion of anyor all or part of such duplicated associated information, where suchrevisions may, in variations, be executed through and in conjunctionwith system and user interfaces such that specific choices may be userdriven and/or user assisted and/or fully automated. This associatedinformation can, in variations, include (but may not be limited to):time-stamps, event markers, system and user generated annotations suchas comments, execution logs, and user created notes.

In some implementations, however, as may be seen in the context of thecurrent example, the cloned version of the original model-set (OutcomeModel Book Cat v2) and its progenitors as well as any part of or all theinformation associated with any of the foregoing elements may remainassociated with the original user (the collector), but, deploying one ormore procedures and interfaces within APM, may also or instead beassigned—that is, to have ownership of one or more elements in theforegoing imputed to another user or a group of users. This is anelement of the identity management aspect of APM wherein, using userinterfaces, ownership properties of any entity may be changed or editedor otherwise modified. APM facilities may also, in variations,facilitate and manage assignment of co-ownership amongst two or moreentities, such that changes to one or more elements composing and/orassociated with any of the models, any set of progenitors of such modelsand/or to any aspects within the collection of ancillary informationassociated with any such elements may, in variations, be executedexclusively (that is, with respect to one or more co-owners but not toothers) or conjunctively (that is, with respect to all co-owners), butsuch that any such changes may be subject to conditions and/orconstraints managed within the RAMS facilities.

As may be seen, the current example illustrates how various and (in someembodiments) diverse DTS methods and procedures may, through a varietyof interfaces and integrated methods implement a close binding of userownership with system entities and that these associational propertiesare managed and administered by the APM control fabric. The APM identityoverlay permits, in implementations, operational infusion of additionalinformational depth and complexity to even seemingly-simple operationssuch as duplication of previously rendered results, and the enablementof such Shannon entropic extensions and alterations also provides onebasis for permitting DTS and DTS-enabled systems to provide additionalenhancements of and extensions to the seminal notions of AssertionalSimulation.

The role of APM associational properties may be evident in the currentexample as new, succeeding Outcome Model instances are created by thebibliophile. Newly spawned Outcome Models and the newly mintedcontributory models and associated information, however generated andirrespective of the composition of any such instance, are collectively(and, in some variations, individually) assigned new and unique APMidentities, one or more new monikers (optionally provided by a userthrough interfaces) and are newly indexed to one or more (possibly new)users (or system processes). The breadth of this informational genesismay be seen even in the case where a duplicated model is functionallyidentical as referenced in the foregoing, but the relevance and utilityof the APM identity management may assume a different relevance whensuccessive versions embed new or different information—that is, when thedifferences between versions are truly permuted in a computationallymeaningful way, and thus, wherein the differences reflected in distinctidentities are tangible.

Returning to the current example, suppose the bibliophile initiatesanother iteration of his Assertional Simulation categorization projectand decides to substitute non-null values for the null values previouslyassigned to the new categories. He can do this by editing a copy of theAssertion-Apportionment pair that was used to produce Outcome Model BookCat v2 and initiating an Assertional Simulation to produce an altogethernew, alternately parametrized Outcome Model. Alternatively, he canmerely edit the existing Assertion-Apportionment pair. Though in thiscase, no user ownership characteristics have been changed, a new indexand required revised moniker of the Assertion-Apportionment pair maychange. Also, one or more elements within affiliated information mayindeed change, including, for example, any or all of the logs, systemannotations, and user generated notes. The new Outcome Model mayoptionally be re-named, but for pedagogical convenience, let its nameremain Outcome Model Book Cat v2.

Suppose, however, that the user instead wishes to retain the originalOutcome Model Book Cat v2 (with null assignments to the new categories).In this case, DTS, in variations, may provide a number of options: thecollector could have opted to clone and modify Outcome Model Book Cat v2before making the foregoing changes thereby creating another versioncalled Outcome Model Book Cat v3 which is also functionally identical tothe original version, Outcome Model Book Cat v1, and then make thepreviously described changes to either of the cloned Outcome Models.Alternately, Outcome Model Book Cat v3 may be created from the initialversion (with fewer categories) and then modified to conform withanother Outcome Model. In either case, there would be 3 versions ofOutcome Models (and 3 Assertional Simulation chains, each with itsunique progenitors and associated information) but since, depending onthe choice of parentage, the ancestry will differ, the specific elementswithin the associated information will differ, including, at the veryleast, for example, references to the progenitorial models, though otherelements may change, as well.

This example of DTS-fostered creation and mutation of elements composingAssertional Simulation illustrates the deep integration of the APMidentity and associational services within methods and proceduresthroughout the system. Any or all of the previously cited APM modalitiesand modes of operation may be deployed in these and similar operations.But as may be seen in the current, relatively simple example, themethods and procedures and interfaces composing APM (and more generallyDTS) enable users to create and maintain detailed records of the genesisand, in some contexts, the progressive mutation of versions of theelements composing an Assertional Simulation. In variations, of course,the depth and degree of detail and even the type of informationpreserved within and provided as a result of such record-keepingoperations may differ depending, for example, on context, user input,and preference.

One consequence of the integrated preservation of granular and detailedprogenitorial data and information is that users, groups of users andsystem processes may optionally initiate and manage, document and, invariations, modify and/or augment a variety of creation related recordsindexed not only to the created elements but to the associated users,groups of users and/or system processes. These capabilities may beuseful in a variety of DTS applications where the integrity of data—whatcould be called chain-of-custody validity such as may be used orrequired in audit trails or forensic accounting—related to suchinformation may be important. In less stringently regulatedimplementations, minimal creation and modification records such asuser-generated and user-maintained change logs and system moderateddate-stamps may be useful.

Note, however, that in many applications, DTS users often createmultiple Outcome Models and the optional APM fostered preservation ofthe precursor models and other elements in these Assertional Simulationchains of Outcome Models through one or more of the cited optimizationtechniques and, as shown in these examples and in the foregoing exampletechniques (and in other applicable procedures which, in variations, maybe optionally invoked as an ongoing or piecewise-ongoing process)reveals an aspect provided within Assertional Simulation and thesupporting DTS-based tools in general, but through the permutational andidentity tracking aspects embodied within APM and, in variations, otherDTS-based services, in particular. Specifically, application of thesetechniques may, in many applications, inherently enhance the breadth,variety, density and referential scope of the information distributedthroughout the array of previous generations of models, their elementsand their by-products. In this manner, each such iteration may, invariations, increase the Shannon entropy embodied within this historicalcollection by injecting new, often but not necessarily in allvariations, uncorrelated information.

The increase in the Shannon entropic richness derived from the specificmanner in which DTS-based and APM related methods manage the iterativeprogression of generations of model chains (as cited in the foregoingand other parts of this disclosure) is important. In variations,DTS-based and other non-DTS but system accessible analytic techniquesmay be applied to specifically leverage this Shannon entropicenrichment, including (in a non-limiting example) methods that may beexecuted in the context of (for example) one or more layered recursiveoptimization routines which may contextually and selectively harvestcertain information elements embedded in these collections, and maycalculate or infer additional information that may be directly derivedfrom this embedded information or which may be extracted from one moreother sources (such as, for instance, from some representational idealrelated to one or more elements in the Outcome Models), and may theninject this new, possibly uncorrelated information into one or moreelements within the current model chain, thereby possibly producingaugmentation of the results on the optimization curve. In other examplecases, this information and other analytic capabilities may be madeavailable to DTS users, through one or more selection and reorganizationinterfaces, so that skilled users may manually inspect and mine theinformation in pursuit of some objective.

The relevance of this inherent increase in Shannon entropic richness maybe seen throughout the operations of the DTS platform and within citedexamples. The final part of the volunteer example highlights thepotential use of APM fostered preservation of information as shown whenthe committee members leverage APM managed information preservation;using methods and procedures and interfaces drawn from DTS-based (and/orDTS-enabled and/or DTS-accessible) data structuring interfaces to formatand/or transform or otherwise manipulate or prepare data to be presentedto statistical analysis and pattern detection facilities, the user mayinvoke a variety of analytic and pattern detection techniques todiscover undetected characteristics that may have contributed to thecreation of a consensus-accepted successful Outcome Model from the past.This example points to a common problem in some real-world environments,however, one which comprises an important factor in the successfulconstruction of optimal Cardinal Outcome Models: the role of hiddeninformation in the optimization of Assertion-Apportionment Models and inthe selection of Cardinal Outcome Models. The book collection exampledescribes the role of the APM fabric in the enablement and support ofDTS-based permutational variations, DTS-based clone and modifytechniques and other DTS information generation methods, including, forexample, preservation of intermediate and/or transient results withinoptimization loops. A DTS Platform provides methods and procedures andinterfaces that provide users and system processes broad capability toprepare and present structured (and restructured) information toadvanced techniques both integral to or accessible to a DTS Platformdesigned to discover otherwise undetected information that may berelevant in decision making processes.

Thus, as discussed in the foregoing, Assertional Simulation not onlyproduces an analytic product in the form of Outcome Models (in thatthese are structures that embody the application of structured andparametrized assertions to commonly-accessible reference data whichembodies an analysis that reflects a view of “ground truth”), but also,in variations and in some contexts, provides techniques that may be usedto additionally (or alternately) present Outcome Models (and inembodiments, any aspects of the antecedent Assertional-ApportionmentModel pairs) to other types of analytical procedures, including (but notlimited to) those non-limiting examples cited in the volunteer example(statistical analysis programs and pattern detection techniques). Butnote that the effectiveness of these non-limiting example analyticprocesses (and others that may be relevant) deployed in the context ofDTS processes that increase the Shannon entropic richness of informationsurrounding and related to Assertional Simulation (as cited in theforegoing and elsewhere in this document) enhances and extends thevariety and efficacy of such analytic products. This example exemplifiesone way in which DTS methods, procedures and interfaces providecapabilities that magnify processes and procedures common in manybusiness applications.

These capabilities may be seen in the application of DTS to commonbusiness problems. Suppose a manager responsible for Profit Center Aconfigures a series of budget forecasts covering the following year byusing the previously described clone and modify facility (described inthe foregoing), and by using a group of Cardinal Outcome Models fromprevious years as the basis upon which to compose a final proposed nextyear projection. In this series of forward projections, the manager usesDTS interfaces to not only make changes to certain values within theassociated Assertion-Apportionment Model pairs but creates scaledversions of previous year Reference Data Models to form the basis of thecost and revenue aspects of the budgetary projection. After producingOutcome Models using these parameters, by projecting the cloned andmodified Assertion-Apportionment Model pairs upon the cloned andmodified Reference Data Models, the manager is pleased to note thatthere are tangible improvements in the results for profit center A butsuspects that further optimization may be possible but does not knowexactly how this might be achieved.

Suppose, moreover that this manager employs the DTS-based capabilitydescribed in the volunteer example to prepare and format data fromprevious years (as in the previous example) for submission to bothDTS-based and DTS-accessible analytical and pattern detection programs.The objective in this case is not to validate the veracity of thechanges already proposed by this manager (this has been reflected andproven in the improved results shown in the already produced forwardprojections) but to execute a type of blind pattern detection to findwhether further optimizations might be made if the currently proposedchanges had been made in the past. One underlying notion in this exampleis that the manager wishes to leverage DTS and Assertional Simulationcapabilities, and most important, to attempt to take advantage of thedepth and breadth of the Shannon entropic enhancement engendered byrepeated cycles of APM supported Assertional Simulation over time, asdescribed previously. The manager's suspicion is that analysis of pastyears may uncover hidden relations which might be used to improveperformance, changes that the manager hopes will be possible given theadditions advocated (and verified) in the just-executed forwardprojection.

In pursuit of this objective, using DTS methods and procedures, themanager selects, clones, modifies, and formats a series of CardinalOutcome Models and their progenitorial Models from past years, makingcertain that the modified Assertion-Apportionment Model pairs reflecttheir proposed result. In this case, however, the manager submits notonly the Cardinal Outcome Models from past years and the associatedprogenitorial models and any related ancillary information but alsoOutcome Models (and related contributory models) that were not selectedas the Cardinal Outcome Model sets—that is, the manager also includesall the DTS-named Candidate Assertion-Apportionment pairs and therelated Reference Data Models and Outcome Models.

Note, therefore, that this manager is seeking to advocate a ground truthverified, in their estimation, by projecting into the future existingOutcome Models using statistical techniques (such as linear regression).But, having validated this assertion (to their satisfaction), themanager may then create a series of backward-looking “what-if”projections by first inserting the “now-proven” set of changes intocloned but modified Cardinal Assertion-Apportionment Model pairs andprojecting these changes upon unmodified versions of past CardinalReference Data Models, thereby seeking hidden information that mayreveal additional benefits given the value of the proven changes. Note,however, that as with the earlier example describing the DTS-basedanalysis invoked by the volunteer organization, wherein the indexing andpreservation capabilities of APM were leveraged as input to executevarious types of analysis, in this instance, such richness and depth andvariety of information may, for example, be included to modify one ormore elements of the Assertion-Apportionment pairs, to influence ormodify even the assumptions guiding the parameters applied to thefunctions executing forward projection of cloned Reference Data Modelsas well as employed in other evaluative and normative aspects of theevaluative process.

Based upon methods and procedures described in the foregoing, andleveraging the Shannon entropic richness fostered by the APM identitymanagement capabilities, as described, suppose that the analysis andoptimization routines reveal in the resulting backward-looking andre-calculated Outcome Models that, if the proposed changes had been inplace, the average profitability of Profit Center A would have beenadditionally maximized if the cost (and allocation proportions) of twochart of accounts items were reduced. But in this example, these itemsseem, at least in the mind of this manager to be utterly unrelated andare located in very different parts of the chart of accounts hierarchy.Upon repeated invocation of this backward-looking projection, moredetail emerges, and the manager discovers that profit center A wouldhave increased its profitability 3.2% in previous years by increasingthe cost of the two chart of accounts items at a certain ratio but onlyif the total new expense does not exceed a certain amount of theirrespective parent node totals.

Using the tools provided within DTS and applying Assertional Simulationand various methods and procedures and interfaces described in theforegoing, the manager implements the newly discovered ground truth bycloning the parameters within now proven Assertion-Apportionment Modelpairs and projecting these Outcome Models upon the previously usedforward-looking Reference Data Models.

These examples demonstrate the utility of the novel capabilities withinDTS and the manner in which inherent qualities within the AssertionalSimulation that formalize user expertise and experience-based judgementsin a systematic, multi-faceted computational environment to uniquelyaddress complex and practical real-world problems in many professionaland social contexts.

The final element in the DTS Entity-Management Fabric is called DTSTopical Capability Mediation (TCM). TCM is a broadly-based operationalcomponent providing functionality related to and, in instances andembodiments, interconnected with the operational functionality withinand associated with RAMS and APM. In some operational modalities, TCMmay re-purpose and utilize some RAMS capabilities as instantiated in therelevant methods and procedures and interfaces but may also employunique-TCM computational components. In addition, like RAMS, TCM may, inembodiments, utilize, access, leverage and share one or more elements ofthe entity identity-related services and methods and procedures andinterfaces provisioned within APM, including any or all capabilities andvariations cited in and implied this disclosure but those that may beinstantiated in embodiments and those that may be variations andderivatives. But note that, as with the interoperability APM and RAMS(referenced in the foregoing descriptions and in other parts of thisdisclosure and as instantiated in concrete implementations), one or morecomputational components of TCM may, in embodiments, also deliver someor all of its described series and functionality in a standalonefashion, fully or partially instantiated within or associated withnon-DTS systems and/or environments and thus, may operate independentlyof either a DTS Platform 1000 and/or in conjunction with either DTS- andnon-DTS-based APM and/or RAMS instantiations, noting, of course, that insuch instances, one or more computational elements from APM and/or RAMSmay be present within an independent TCM instance.

On this basis, therefore, note that while TCM may interoperate with andmay share one or more methods and procedures and interfaces with bothAPM and RAMS and may re-purpose others), TCM differs from RAMS in thatRAMS functionality (in the most general sense) is configured toadminister execution of system-based processes and TCM is configured toadminister entity engagement with various topics, including withoutlimitation system-defined topics. Thus, from at least one perspective,RAMS, as described in this disclosure may be seen, in embodiments, as anovel instantiation of an extended, multi-layered, context-responsiveand event-triggered access and process execution mediation mechanism,while TCM provides, without limitation a topical mediation mechanismapplied to the subject entities where its intermediation mechanismsadminister whether (and under what conditions) the respective subjectentities may associate with respect to or in the context of one or morediscrete topics, such as system-defined topics or subjects—or, using DTSterminology, in respect to DTS Transactions. Note, however, that theterm “DTS Transaction” may optionally refer to system operationsinvolving the exchange of units but rather, refers more generally tointeractions of any type centered about one or more topics, such assystem-defined “topics” and the like. Therefore, RAMS may administerentity capabilities and actions with respect to execution of systemoperations while TCM may administer actions and capabilities of entitiesto associate in respect to a given topical context, such as asystem-defined topical context.

Thus, in DTS, an entity may have at least two modalities ofassociational, access and execution capability, each administered bymethods and procedures and interfaces associated with or subsumed withinthe operational and execution schemata of RAMS or TCM or other suchmediation facilities: 1) RAMS-administered System Capabilities (orsimply System Capabilities) and TCM-mediated Topical Capabilities (orsimply Topical Capabilities). An entity may, for example, have a suiteof associational and execution rights RAMS-based System Capabilitieswith respect to a collection of other entities but may have a differentset of Topical Capabilities with respect to those same entities. Theseassociational, execution and other system activity rights andcapabilities represent the combined functionality of the DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces within or associated with the constituents within thepreviously referenced DTS entity-management fabric, APM, RAMS and TCM.

In a simplified example of RAMS-administered System Capabilities andTCM-mediated Topical Capabilities, suppose an entity 1 (E₁) possesses aset of System Capabilities (SC₁) and entity 2 (E₂) possesses another setof System Capabilities (SC₂) and that, through RAMS mediation (asdescribed in detail in later sections of this disclosure), E₁ and E₂possess a set of system associational rights reposed within a data storeSC^(A) ₁₋₂, where the superscript “A” signifies that this informationaggregate deals with system-related associational rights only (and notexecution rights) and the subscript “1-2” signifies that thisassociational data applies to entities E₁ and E₂. Assume further thatdata store SC^(A) ₁₋₂ contains the RAMS-generated mediation informationthat unconditionally permits entities E₁ and E₂ to associate with oneanother with respect to certain system operations enumerated (orreferenced) within data store SC^(A) ₁₋₂, a capability that, as statedis, in this example, merely associational and does not reference orenable execution of any specific operations by entities E₁ and E₂, inembodiments, a separately mediated capability, also administered byRAMS. But in this simplified example, let T_(S) refer to asystem-defined subject or topic and let E₁ and E₂ possess a set oftopical associational rights reposed within TC^(S) ₁₋₂, where TC refersto Topical Control, as administered by methods and procedures andinterfaces within or associated with TCM and where, as before thesuperscript “S” signifies that this information aggregate deals withtopic-related associational rights defined for T_(S) only (and not otherpotential rights) and the subscript “1-2” signifies that this topicalassociational data applies with respect to entities E₁ and E₂. Forsimplicity, assume that the topical associational capability within datastore TC^(S) ₁₋₂ fully enumerates the associational capabilities ofentities E₁ and E₂ with respect to topic T_(S) and further, that thereare no other topics defined in or constrained by the TCM system apartfrom T_(S). In this example, therefore, absent changes in theassociational data store SC^(A) ₁₋₂, entities E₁ and E₂ may associateunconditionally, irrespective of any system-defined topical context andmay seek to initiate and/or execute any system activity, subject to theRAMS-administered, event-triggered mediation that may apply to thoseactivities, except with respect any association executed that involves(or, in embodiments) is related to topic T_(S). In this case, thetopical associational rights reposed within data store data store TC^(S)₁₋₂, as instantiated and maintained by methods and procedures andinterfaces within or associated with the TCM system (and, in somecontexts and in embodiments, in association with methods and proceduresand interfaces within or associated with APM or other such components)provide the parameters by means of which TCM (and, in embodiments, othersystem components) execute the topic-based, entity-referencedassociational mediation action for entities E₁ and E₂ with respect totopic T_(S). The results of these collective actions, the decision as towhether entities E₁ and E₂ may associate in the context of topic T_(S),depends upon factors that include (but are not limited to) suchconsideration as the specific constraints and conditionals that may beenumerated within TC^(S) ₁₋₂, the nature and composition of topic T_(S),and other system and entity-related considerations, considerationsmediated within and in connection with the enumerated capabilitiesreposed within associational data store TC^(S) ₁₋₂ as well as otherrelevant information E₁ and E₂.

As those familiar with these and related practices may see, the combinedcapabilities of TCM in conjunction with those provided by RAMS and APM,both in the context of deployment and operation within a DTS (orDTS-accessible) Platform 1000 and in non-DTS environments, provide abroadly-based and versatile suite of context and content-sensitive andcontent-responsive access and execution control options that, inembodiments may be exerted upon users, data and system processes. Thesemulti-leveled, multi-variate and variegated control schemes includehighly-tunable conditionally-applicable regulatory mechanisms that, inembodiments and at user and system option, may include (in some caseswithin the same instantiation of a Platform 1000) lightly-administeredmediation involving broadly-defined system-related and topically-definedrights and capabilities applied to and upon grossly-differentiated andbroadly-grouped entities as well as tightly controlled mediation withvery finely closely defined topics and exerted applied to and uponhighly-differentiated entity groupings. In addition, such a range ofvariability may not only coexist within the same DTS Platform but mayalso be conditionally and with contextually- and/or user- and/orsystem-meditation be applied flexibility even upon and amongst the sametopics and entities.

FIGS. 1-4 and their elements provide a high-level illustration of anarchitecture and organization, plus the interaction of variouscomponents, systems, processes, interfaces, functional blocks, controlsignals, data streams, data elements, and the like, for a DTS Platform1000 for Assertional Simulation. The DTS Platform 1000 is referred to insome cases herein as a “DTS Assertional Simulation” Platform, the DTSSystem, or simply as “DTS,” and each such term should be understood toencompass a system that uses one or more embodiments described herein,including the various elements depicted in connection with FIGS. 1through 4 and their parts as well as with other figures and descriptionswithin this disclosure. These and other figures (and the relevantdescriptions) depict but one embodiment (or sets of embodiments) of aDTS Platform 1000 architecture, noting that other architectural choicesmay be made in alternative embodiments and that variations inimplementation of operational characteristics composing the teachingsembodied in the DTS Platform 1000 as may be understood by those skilledin the relevant arts. as well as various.

Note, of course, that in the same sense, these (and other) diagrams andaccompanying and related descriptions are illustrative and intended toexemplify the principal teachings embodied in the Platform 1000. Assuch, these depictions are intended to encompass alternativeconfigurations and implementations as would be understood by one ofskill in the art in light of this disclosure.

Further, since the following paragraphs reference graphicalrepresentations of details of possible implementations of varioussystems, subsystems, components and other functionality that maycomprise, integrate with, incorporate, use and/or interact within and/orupon the DTS Platform 1000, including a wide range of embodiments,combinations and variations that are enabled by novel aspects of the DTSPlatform 1000, some depictions of various control and datainterconnection mechanisms and characteristics related to and/oraccessible to and/or from constituents within DTS Platform 1000 may beeither generalized and/or combined (except where context discouragessuch representational simplification). These choices are made for bothgraphical as well as pedagogical purposes and should not be inferred aslimiting or excluding alternate implementations. Such elements includebut are not in all cases limited to any or all of the following:command-type control; informatics-based control (which may, for example,provide input to conditional logic and/or variable values to formulae);user-facing and/or user-based and/or user interface control mechanismsand information; data, certificates, cryptographic strings andsignatures as may be related to (among other things) limitation orgovernance of permissible operations and/or access; and/or reference todata elements within or accessible to one or more sections withinplatform-based or platform-enabled methods and procedures. References tocontrol and data interconnection elements throughout this disclosure andin the attendant figures should be understood to optionally use orencompass any or all of these capabilities as well as others that may beinferred as applicable, except where the context indicates otherwise.These mechanisms are outlined in general in the following paragraphs,and references throughout the disclosure should be understood toencompass any of these capabilities, as well as other capabilities thatare presented elsewhere in this disclosure, except where the contextindicates otherwise.

Referring to FIG. 1, DTS Platform 1000 is illustrated as organized intothree primary architectural operating blocks, described as integratedcompound functional blocks, shown (in no particular order of precedence)as DTS Control Mechanisms and Operating Interfaces 1101; AssertionalSimulation Parameterization 1102; and DTS Assertional SimulationOperations 1103. Note again that, as stated in the foregoing, inimplementations and variations of a DTS Platform, this aggregation andits constituents may (without limit) be arranged, reorganized, reorderedor presented in other alternate but functionally equivalent (or similar)configurations and thus, the present illustration (and others) does notlimit the applicability of such alternative graphical representations tothese and further descriptions.

While FIG. 1 subsumes constituent functional elements explicitly, FIGS.2-4 (and related figures) expand these representations, revealing moredetail regarding implementation functionality within a DTS Platform1000. Thus, FIGS. 2-4 (and related figures) remove the explicitfunctional groupings (1101, 1102 and 1103) but essentially retain thenominal (but not determinative but rather expository and illuminative)architectural geometry represented in FIG. 1. As may be seen, forexample, in FIG. 2, a more detailed but still high-level representationof the operational aspects of Assertional Simulation within a DTSPlatform 1000, functional block Manage Selection, Formatting, Processingof Models for DTS-based/DTS-enabled Analysis and Visualization 1119 isretained from FIG. 1 but placed in a more detailed operational context.

This approach is utilized throughout the presentation of the followingfigures and descriptions together with the generalization andcombination of system-wide functionality of such pervasive elements ascontrol signal/data flow elements, as mentioned in the foregoing. Mostrelevant in this connection is the composite control signal/data flowPlatform-wide Controls and Interfaces 1111. This functionality is shownin FIG. 1 (and in the following figures) as representing a composite ofrelated but discrete control signal/data flows (1108A, 1108B, 1109 and1110) when in practice and in variations these (and other sub-elementswithin these signals) may not contextually combined (or presentedadjacently) as shown here. This multi-layered composition of compoundinformation aggregation is, as in the functional groupings mentioned inthe foregoing, presented for graphical and pedagogical and illustrativepurposes as may be understood by those versed in the relevant arts, andshould not be understood to represent a required association.

Returning to FIG. 1, and bearing in mind the foregoing, composite blocks1101, 1102 and 1103 group collections of aggregated computational,control and interface functionalities which, in turn, containrepresentations of functional components also arranged by generaltopical functionality, noting again, that the depicted arrangementshould be understood as accurate but primarily pedagogical and thusillustrative in order to convey the functional role of these componentsand should not be interpreted as consequential to any requirement oroperation within a DTS Platform 1000.

Integrated compound functional block DTS Control Mechanisms andOperating Interfaces 1101 groups certain functional blocks mainly butnot exclusively depicting low-level System operations such as (withoutlimitation) control information, status information, initialization androuting control data. The items included in 1101 representfunctionality, interface components and executable elements that are, ingeneral (but not exclusively) present and available to many otherfunctional elements that may be present within and/or availablethroughout Platform 1000. This functionality includes (withoutlimitation and in no particular order of precedence) compound functionblocks Manage Internal User Control, Interfaces and External Data andAPI Access 1104; Manage Looped, Iterative and Recursive Operations 1105;Manage Recombinant Access (RAMS) and Topical Capability Mediation (TCM)(RAMS) 1106; Manage Activity Logs and Metrics 1107. Integrated compoundfunction block 1101 is shown as providing outputs signal (1108A, 1108B,1109, 1110), control flows that, as in the foregoing, representcomposite information aggregated as described. Note, however, that someor all constituent flows subsumed within these signals may bebi-directional in nature but since they represent compositefunctionality may not flow in both directions in all cases. In any case,for reasons outlined previously, 1108A, 1108B, 1109, 1110 are shownflowing in one direction. Note this convention will be followed in otherfigures except where context requires bidirectionality. Note furtherthat, in general, but not exclusively, a control flow may be placedbeneath but not connected to an associated functional block subsidiarywithin 1101, a graphical depiction shared in 1102 and 1103. Thisrepresentation has been chosen for graphical and informative purposes inorder to convey that the referenced control/data flow signal may alsoconnect to and provide data and control (and other information) to andfrom blocks within the same enclosure. Thus, for example, User andSystem Control 1108A is shown beneath Manage Internal User Control,Interfaces and External Data and API Access 1104 but clearly usercontrol is required for the other blocks within 1101.

Compound function block Manage Internal User Control, Interfaces andExternal Data and API Access 1104 represents a collection of methods andprocedures that manage and control user-centric functionality includingbut not limited to user identity, user profile and status, where suchfunctional elements may be present and utilized throughout Platform 1000and which may permit users to execute such example functions (withoutlimitation) as methods to interact with the System, methods toparameterize Assertional Simulations and the elements within thoseoperations; interfaces and control mechanisms between system elementsthat effectuate the interconnection of the elements of AssertionalSimulation; such other user-based, user-related and system-drivencontrol mechanisms that may include, without limitation, any or all ofthe following: user activity related to the primary aspects ofparameterizing, modifying and otherwise interacting with any of all ofthe aspects related to Assertional Simulations and ancillary operationsas described here; user-facing interfaces and interactive componentsthat communicate information to and from users for nearly every aspectof system operation; positive-input and fully user-based interaction(such as enabled by any number of graphical interfaces) wherein the usermay make and instantiate control selections and decisions;process-assisted control types such that control parameters may beinitialized and/or modified by system processes, but also such that thecontrol exerted by a user or a process may be assisted by and/ormodified by a process (which may be a separate process in the case ofassisting a process); user-assisted control types such that controlparameters may be initialized and/or modified by other users, such thatthe control exerted by one user or by a process may be assisted byand/or modified by a user; semi-automated processes wherein users may(optionally, depending on context) monitor any execution progress (atvarious intervals) and may optionally inspect, intervene and adjustoperations, but such that in certain operating conditions (such asnormal operations) a user does not need to do so; and fully automatedprocesses wherein, regardless of how a process may be initialized, itsexecution is system driven with little or no user intervention.

Included in functional block 1104 are methods and procedures andinterfaces by means of which external data to and from informationsources and computational functionality to and from remote facilities(that is, facilities separate from Platform 1000 itself) may connect to,interface with or may otherwise be made available to or from thePlatform by means of one or more (optionally) bi-directional API'swherein additional/and/or adjunctive (and/or even redundant)computational capability, data or other information and otherfunctionality may be made available to any or all computational aspectand executable blocks within Platform 1000, where such API's may providePlatform 1000 (or may be supplied by Platform 1000) with services and/ordata as in the foregoing and without limitation continuously,intermittently, on-demand, implicitly (as within a collection ofdistributed capabilities and subsequent demand) and/or explicitly (underimplicit and explicit user and/or programmed control).

Information flow that may be related to and/or which may be generated orotherwise transformed or changed by but which may not be exclusivelyaccessible to Manage Internal User Control, Interfaces and External Dataand API Access 1104 (and others not listed or shown) are shown in FIG. 1and in other depictions as composited within control signal/data flowUser and System Control 1108A. Signal 1108A primarily but notexclusively subsumes control signal and data flow related to informationgenerated or processed within 1104 (as in the foregoing), but asmentioned, information embedded therein may also be available to anyother blocks within 1101 (even those not explicit but related), andindeed, unless otherwise indicated and otherwise contextually prohibitedor infeasible (as one versed in such arts may see), such functionalitymay, in implementations, be present and embedded within other functionaland composite blocks and should assumed to be present even when notexplicit. Moreover, as described, 1108A is subsumed within controlsignal/data flow Platform-wide Controls and Interfaces 1111 by means ofwhich the foregoing functionality as well as others not mentioned areshown to be and understood as available throughout the system.

Continuing to examine integrated compound function block 1101, blockManage Looped, Iterative and Recursive Operations 1105 an architecturalfeature that may be optionally integrated in implementations ofoperational aspects of a Platform 1000. Block 1105 is closely but notexclusively bound to control signal/data flow 1108B, where its specificgraphical placement is so-chosen as described previously. 1108B is avariegated and complex instance of this type of data transformationaldata/control signal and is often integrated with or connected to (and ininstances) may enable and control advanced functionality provided withina DTS Platform 1000. In one simple example, a comparison-basedoptimization routine may be deployed as a part of or integrated withincertain normalization routines (as may be present in embodiments ofPlatform 1000), wherein target data is recursively modified in a loopingoperation and compared to a different parametrized instance of thattarget data such that the looped modification continues to compare andmodify the target data until the target data converges to the closestapproximation to the parametrized instance. The previous example is, ofcourse, an elementary instance of routines of this sort and are thesimplest representation of this class of algorithms which include, forexample, generic and competitive technologies and other complex AI-basedevolutionary routines. In embodiments and within variations, Platform1000 utilizes a variety of such programs and utilities.

Note, however, that here and in other instances in this description, thegeneric term “optimization” may refer, in general but not exclusively torecursive or self-modifying techniques but may also (and/or instead) beused interchangeably with the term “iterative/recursive looping”. Notefurther, however, that the functionality of the Platform 1000 is notdependent upon such optimization nor upon recursive or self-modifyingtechniques. While recursion features can enable various usefulcapabilities in embodiments and implementations of a DTS System 1000,there are implementations of DTS where few (if any) operations involverecursion techniques. Thus, to the degree that recursion techniques arepresent, they may be employed in various alternative ways to the mannerin which they are depicted in the present description, such as, innon-limiting examples, in server-side database access routines andpattern detection algorithms.

The presence of control signal/data flow Optimization Control 1108B(which may optionally be included within Platform-wide Controls andInterfaces 1111 or shown explicitly) conveys the optional presence ofthis capability within and availability to elements composing a DTSPlatform 1000, where such integration may be system-wide or withinparticular components. Operations that may be included or accessible tosuch signal/data flows include (but are not limited to): iterative,looping and recursive revision methods involving feedback-driven (and insome cases, feed-forward-driven) modification of data, informationalelements and in some instances, parameters used by and within methodsand procedures; revision and adjustment of the structure and operationsof those procedures themselves, based fully or in part on the result ofthese earlier operations in such a manner that the output collection ofone or more computational procedures composes and/or is integrated withother information to form the input to a loop-based repetition by and/orwithin methods and procedures.

Thus, Platform 1000 may include, in embodiments, system-wide embeddingof recursion-based modification of system elements in various modelingand simulation sub-systems, including in an overarching AssertionalSimulation modality. Moreover, embodiments of 1105 and 1108B may includenot only recursion-based revision, but other related methods and formsof information modification, including but limited to: context- andcontent-derived self-modification of data; revision of method andprocedure and interface structure; changes in and/or modulation ofexecution parameters.

In this connection, therefore, the descriptions and diagrams in thisdescription may make repeated references to such self-modifying and/orself-referencing revision-based operations where such (generally) loopedtechniques may be based upon and/or may include parameters related to(but may not be limited to) any or all of the following: resultsobtained from (and/or which may be inferred from) one or morecontent-based and/or content-derived technique; and/or results obtainedfrom (and/or which may be inferred from) one or more context-basedand/or context-related revision techniques.

Note again, however, that 1108B may be shown as a component withinPlatform-wide Controls and Interfaces 1111 and thus, when 1111 issignified, implementations may include any or all such capability andattendant control signals. Note also that any or all of the functionalcomponents (and the subsidiary methods, procedures and interfaces) aswell as the data that may be related to or derived from or inferred fromsuch executable elements may implement and/or may participate in and/ormay contribute to such iterative and recursive techniques (which maychange or revise any of all of the elements outlined in the foregoingdescription) using any number of known techniques and may also (inaddition or instead in variations) deploy such techniques.

The functional aspects captured within such computational ortransformative processes as may participate (or may be able to do so),as in the foregoing, and referred to here as receiving and responding toOptimization Control 1108B may be also referenced as being engaged in“looped refinement”, a DTS-engendered term. DTS looped refinement mayexhibit (but may not in all cases be limited to) any or all of thefollowing features, execution structures and configurations: a)intrinsic looped refinement wherein some or all of a loop-based revisionis a feature of one or more procedures or methods within a functionalblock; b) extrinsic looped refinement wherein some or all of aloop-based revision is embodied by one or more mechanisms subsumedwithin the composite functional box may interact with Manage Looped,Iterative and Recursive Operations 1105 including composite/controlsignal User and System Control 1108A such that 1108B in this context maybe derived from (one or more) external parameters (Manage Internal UserControl, Interfaces and External Data and API Access 1104) supplied (orotherwise made available) to DTS (or DTS-enabled) methods and proceduresfrom external systems (such as via industry-typical system interfaces,such as API's) and/or other elements within which (in this context)refers to one or more parameters that may have been pre-set globallyand/or conditionally on a per-instance basis by system users or groupsof users and/or which may have been obtained from and/or produced byand/or otherwise derived from one or more DTS-based (and/or DTS-enabled)processes, methods and procedures, where any or all of the foregoingmethods may be executed in combination.

Note further that any or all of the foregoing aspects of DTS-embeddedlooped refinement and the related functional block Manage Looped,Iterative and Recursive Operations 1105 (and the control signalOptimization Control 1108B may be executed in a number of ways,including but not limited to: within and/or between methods, proceduresand processes that may be subsumed within (and/or referenced by and/orindexed to) a composite functional box where the control streamOptimization Control 1108B may be connected; within and/or between otherfunctional boxes—that is, between the composite boxes themselves butalso within and/or between elements within the boxes respectivelycomposing (one or more) nested or enclosed looped refinementarrangements and optionally featuring one or more inner loops and one ormore outer loops such that the informatic domain(s) within (one or more)such inner and outer looped refinement cycles may take place and/or maybe either related or unrelated.

But note as well that any or all of the foregoing may be conditionallyexecuted for any of the reasons outlined in previous descriptions,including those related to content-derived and context-relatedinformation. Such aspects and capabilities and operations within oraccessible to (and/or as may be made accessible to external systems) bythe DTS System 1000 may be enabled, controlled, initiated or otherwisecomputationally related to information embedded within (or related to)composite data and control stream Optimization Control 1108B, elementsof which may be employed in various embodiments described herein aswould be understood by one of ordinary skill in the art. As may becontextually appropriate, additional details relating to particularembodiments of looped refinement (and related operations) may beprovided throughout this disclosure and in concrete implementations ofone or more components of a DTS Platform 1000, but, where not explicitlyshown or cited, should be understood as possible or implied, conclusionsthat may be achievable by those versed in such operations. Forcompactness without loss of general applicability, references to dataand control stream Optimization Control 1108B should be assumed toentail any or all of the foregoing (without limit) depending onimplementations and variations appropriate for various embodiments.

Continuing within compound block 1101, composite functional block RAMSand TCM Control 1109 and control signal/data flow RAMS and TCM Control1109 provide functionality that may permeate any or all elements withinembodiments of a DTS Platform 1000 in a manner similar to previouslydescribed 1104, 1105, 1108A and 1108B. In this case, however, thepotential prevalence of 1106 and 1109 may be more pronounced andubiquitous since the functionality represented within these blocks andsignal flow may, in embodiments, impact not only system operations butdata and user identity, as well, impacting execution and access in bothmundane and complex operations.

RAMS is a DTS-originated acronym for Recombinant Access Mediation Systemand is a generalized reference to a collection of functionality that inembodiments, implements a layered and multi-mode real-time permissionand access control and capability mediation system optionally embeddedwithin a DTS Platform 1000. RAMS and its constituent elements encompassan event-driven, object-oriented, context- and content-derived data,process and user access control and execution access and capabilityinfrastructure which asserts a variegated and contextual control fabricover many aspects of the operation of a DTS System 1000. Note thatdetails on the operation and functionality of RAMS 1106 and the activityof signal 1109 are provided in further sections of this disclosure.

In the present context, however, and as with the other control signalsdescribed in the foregoing, functional block Manage Recombinant Access(RAMS) and Topical Capability Mediation (TCM) 1106, irrespective of itsnominal complexity and ubiquity, may be considered a low-level platformoperation, though in many instances, elements related to the operationand execution of its control fabric may become evident to, accessible toand controllable by users and other higher level entities. Thus, both1106 and signal 1109 are depicted in block 1101 with other “buildingblock” elements though its functionality may be more varied andfar-reaching than others.

As with the other components of compound block 1101, control signal/dataflow RAMS and TCM Control 1109 is shown as placed underneath block 1106but, as before, the functionality composited within 1109 may beavailable to and may act upon any of the elements within 1101, asindeed, as it may with any functional elements within Platform 1000.Moreover, 1109 is shown as optionally embedded within Platform-wideControls and Interfaces 1111, and with the other control signaldescribed in the foregoing in this connection, may be considered presentwhen 1111 is shown, although, it should be noted that one or moreelements of 1109 may be present throughout a Platform 1000 and its lackof explicit presentation should not imply its potential presence orapplicability. While the following diagrams and related text may makerepeated reference to the presence of RAMS 1106 functionality andoperations and to the presence of data and control information depictedby 1109, to the degree that there may be context-based differenceswithin a given application, those differences will be noted inconnection with particular embodiments.

Completing compound function block 1101, function block Manage ActivityLogs and in-App Metrics 1107 and control/data flow In-app Metrics/Logs1110 provide system-wide operational monitoring, compilation and loggingcapability. As with RAMS functionality subsumed within compound box 1106and RAMS data/signal flow 1109 (as, indeed, may be case with otherelements related to Platform 1000 that are depicted in composite block1101), the operations of executed by and provided within functionalblock 1107 and data/signal flow 1110 are generally present throughoutembodiments of Platform 1000 and may be described in operational contextelsewhere in this description. And, as described in the foregoing textwith respect to other control signal/data flows related to 1101, thebidirectional signal depicted In-app Metrics/Logs 1110 is shown assubsumed within Platform-wide Controls and Interfaces 1111 and may alsobe assumed as present when 1111 is shown or implied.

The platform-wide DTS In-App activity resource subsumes and mayinterface with and utilize the capabilities from a variable anddynamically and conditionally assembled context-sensitive collection ofcontent-sensitive and content-responsive activity and executionmonitoring resources. This diverse computational and analytic polyglot,in embodiments, may gather, filter, interpret, transform, compile,combine and otherwise manipulate information that may be directlyprovided and/or elicited, which may be inferred from other informationand/or past and present operating conditions and/or which may be derivedor otherwise extracted from any or all of the above and upon which itmay apply extensive computational capability to potentially disjoint,discontinuous and cross-domain information

The next composite grouping is shown in integrated compound functionblock Assertional Simulation Parameterization 1102. As shown, 1102contains an aggregation of functional blocks: Assemble and Parameterizeand Select Candidate Reference Data Model(s) 1112, Assemble andParameterize and Select Candidate Assertion Model(s) 1113, Assemble andParameterize and Select Candidate Apportionment sub-Model(s) 1114). Aswith other high-level groupings of functional blocks, this particularassemblage is mainly illustrative and pedagogic, although, in this case,the composite functionality represented by these blocks may, inembodiments and in some contexts, be more contextually and operationally(but not always functionally) related than other such collections. Thus,blocks 1112, 1113 and 1114 may have a greater degree of topical andoperational connection and, in context, may be more oftencontemporaneously executed than other groupings in FIG. 1 where, inembodiments, such interoperability is manifested in both commonlyaccessible computational blocks as well as in interfaces betweenexecutable components. The differences, however, are underscored by thenomenclature depicted within block 1102: each constituent block 1112,1113 and 1114 is labeled as responsible for the “assembly”,“parameterization” and “selection” of the respective primary (but notthe only) distinct informatic structures within Assertional Simulationoperations. But, while these composite function blocks are shown toshare common terminology and predicates, the operations executed bymethods and procedures and interfaces within each block possess, inembodiments, distinct and in general, specialized components germane totheir respective roles in the DTS Platform 1000 and the role each playin execution of the diverse modes of Assertional Simulation.

Note, therefore, that while compound function box Assertional SimulationParameterization 1102 illustrates aggregated high-level functionality,this particular graphical composition also depicts the centrality of themethods and procedures and interfaces related to, supporting andsurrounding operations related to the primary informatic entities andrelated ancillary components. On this basis, compound box 1102 should becontextually understood as depicting the most basic representation ofthe elements composing Assertional Simulation operations, one intendedto establish its aggregated system context without (yet) conveying toomuch of its variegated detail. Such detail is presented elsewhere inthis description and within other figures and explanatory text.

Despite the fact that the elements comprising compound function box 1102may operate at a higher and more visible functional level than thosedepicted within compound function box 1101, much of the same explanationfor choices regarding the graphical representation laid out for 1101apply to 1102, as well. Note, for example, the compound function boxrepresentation of composite control signal Platform-wide Controls andInterfaces 1111 as an input/output to 1102 but also that, as inforegoing description regarding interconnection within 1101, thepresence of 1111 indicates that any or all of the signals subsumedwithin 1111 may be accessible or otherwise available to any of theelements within any of the blocks shown in 1102.

While operating upon different informatic edifices, compound blocks1112, 1113 and 1114 provide the similar functionality executed by meansof a variety of user and/or system interfaces. The assembly,parametrization and selection operations executed within thesefunctional blocks may, in embodiments utilize unique variations of thesame basic user interface components since the subject information isdefinitionally and functionally different. More detail and context aboutsuch operations may be supplied later in this document but at thehigh-level explanation conveyed in FIG. 1, it is sufficient tographically convey that the main data structures that composeAssertional Simulation Reference Data Models are distinct butcategorically related.

As shown in FIG. 1, the composite output signal flow Selected CandidateModels 1115 connects compound function blocks 1102 and 1103, where thelatter (DTS Assertional Simulation Operations 1103) representsaggregation of the main operational aspects of the DTS Platform 1000while the former represents aggregation of the main operations relatedto creation, manipulation and selection (and, in embodiments,preparation) of the depicted informatic entities, where the latter areshown as input for the operational execution blocks within 1103. Inembodiments, these structural distinctions may be evident but inpractice, these elements (and the plethora sub-elements and layeredfunctionality) may, in certain implementations, be more closely boundsince the actual interconnection is more nuanced and, in most instances,less sequential. Thus, the intention of the high-level depiction withinFIG. 1 in representing this interconnect in this manner is to provide arepresentation of both the functional role of the main elements in anexecution chain of an Assertional Simulation operation within anembodiment Platform 1000 and the interconnection between these elements.

Thus, as will be discussed in greater detail in other figures and text,blocks 1112, 1113 and 1114, utilizing one or more capabilities that maybe in general but not always integral to (nor in all cases requiredwithin) Platform 1000 (as may be embodied in (but not limited to) one ormore elements in bi-directional control signal/data flow 1111), providecollections of methods and procedures and interfaces within a managed(and in embodiments and in some contexts, system-guided) possiblysequential workflow (but sometimes non-linear workflow) by means ofwhich one or more users and system processes may create, parameterizeand otherwise manipulate and ultimately provide one or more CandidateData Reference Models, Candidate Assertion Models and (optional)Candidate Apportionment sub-Model(s).

As noted, the operations subsumed within integrated compound functionblock 1102 (1112, 1113 and 1114) represent basic elements in one or moreexecution-chain(s) that, in embodiments, may be used to create ormanipulate or otherwise engage in operations within Platform 1000 main(but not exclusively) related to Assertional Simulation and relatedancillary operations. The referenced “assembly, parametrization andselection” processes represent aggregated general capability that, inembodiments, may be deployed respectively to assist users and/or systemprocesses in both simple and complex execution of important tasksrelated to pivotal informatic entities and ancillary informatic andfunctional components such as (but not limited to Assertion Models,Apportionment sub-Models, Reference Data Models and Outcome Models).

In embodiments, these structural and/or compositional configurations maybe selected by means of any number of mechanisms supplied by and/orsupported by and/or otherwise impelled by the results of operation(s)executed by elements with Platform 1000, such as (without limitations)any or all of the following in any combination or sequence: (i) director indirect user interface-based selection which may be based upon manydiverse factors (including (without limitation) positive-directed userchoice and/or preference and/or intuition); (ii) based upon or inferredor otherwise derived from one or more internal parameters supplied byother components within a Platform 1000 and/or from one or more externalparameters where any of the foregoing may be supplied and/or may beprocessed through and/or otherwise prepared, modified or otherwisetransformed by means of one or more internal or external parameters;computational resources or elements within previously describedcomposite box Manage Internal User Control, Interfaces and External Dataand API Access 1104; (iii) direct or indirect results or otherparameters that may be supplied by and/or generated as a result ofand/or inferred from any operations associated (partially or in full)with one or more elements subsumed within composite box Manage Looped,Iterative and Recursive Operations 1105, where such functionality may,in embodiments, be provisioned as an integral functionality within DTSPlatform 1000 and/or may be (fully or partially) external to Platform1000 but which may be enabled, affiliated or otherwise accessible as maybe required using, without limitations any or all of the elementscomposed within Manage Looped, “Iterative and Recursive Operations 1105where example recursive or iterative operations of this type have beendescribed in the foregoing. Note that such interconnection between thelower-level elements represented within 1101 may be subsumed within oneor more elements within the bidirectional composite control flowPlatform-wide Controls and Interfaces 1111.

Compound function blocks 1102 and 1103 are also connected by theoptional bi-directional composite control signal/data flowOptimization/Re-Competition 1120. Note that this signal has two aspectsas implied in its nomenclature: optimization and re-competition. Asdiscussed in the foregoing, the term (and the concept-cluster)“optimization” refers in general (but not exclusively) in DTS-relatedliterature to operations related to various types of looped refinementoperations (as outlined previously). As may be evident to those versedin these arts, however, optimization may also be related to and integralto the competitive, collaborative and comparative elements which may bepresent and operational within embodiments of a DTS Platform 1000. Inthis sense, optimization, therefore, may also encompass DTS operationswherein one or more users (possibly assisted by related manual and/orsemi-automated or automated processes) may invoke and supervise (withoutprogram-driven supervision) comparison and competitive analysis andexercises involving various Outcome Models (and the progenitorialinformatic elements from which they are spawned), optimizationoperations that may in some cases involve (without limitation) manualand/or program-assisted and/or fully automatic iterations. One aspect inthese “manual optimization” operations enabled within a DTS Platform1000 involves explicit (though sometimes program-assisted) manualdirection by one or more users and interpolative inspection in directedefforts intended to achieve one or more goals within one or elementswithin the subject Models, goals that may (or may not in all cases) beevident but which may be discovered during such exercises.

Thus, while optimization operations may, within a DTS System 1000 ingeneral, encompass the previously described program-centrictransformative iterative maximization of one or aspects of any of theparticipating Models (based upon one or more static and/or dynamic orcontextually-derived criteria such that one result may be submission orselection of one or more Cardinal Outcomes (and the progenitorialCardinal Models from which they were created)), optimization in a DTSembodiment may also and/or instead refer to the human-centric and mainlyuser-interface-driven selection of one or more Cardinal Outcomes. TheCardinality of Models and their selection from various Candidate Modelsand the components, methods and procedures as well as system resourceswith a DTS Platform were referenced in earlier discussions and exampleswhere the competitive and collaborative capabilities within embodimentsof elements within a DTS Platform were discussed, and furtherexplication of these aspects are presented in this description inconnection to a number of operational contexts.

For the present purposes of detailing the elements within the high-levelrepresentations in FIG. 1, note that bi-directional control signal/dataflow Optimization/Re-Competition 1120 represents both aspects ofDTS-fostered optimization and subsumes the requisite composite of data,information and control elements to support such operations. Moreover,as in the foregoing with respect to compound function blocks 1101 and1102, control signal/data flow 1120 (and its constituents) are availablewithout limitation to any or all of the composite functional blocks (andtheir integrated constituents) within compound function block DTSAssertional Simulation Operations 1103. Finally, note that controlsignal/data flow 1120 not only “feeds back” to the functions within 1102(such that elements within that control signal/data flow may be used byany or all of the blocks within 1103, without limitation) but that 1120also connects to the output of 1102, control signal/data flow SelectedCandidate Models 1115, a composite output described in the foregoingtext. In this manner, FIG. 1 depicts the high-level interconnection andinteroperability of these elements and functionality to which they areconnected which, in practical terms, means that the embeddedfunctionality within these composite boxes may be accessed in any numberof ways through embedded interfaces within the collection methods andprocedures.

FIG. 5 (and related figures and descriptions) depicts example workflows,data and information flows as well as methods, procedures and interfacesassociated with the creation, assembly, and maintenance of ReferenceData Models. This combined functionality is represented at a high levelin FIG. 1 through the depiction of composite functional block Assembleand Parameterize and Select Candidate Reference Data Model(s) 1112,wherein a suite of methods and procedures and interfaces are shown assubsumed within integrated compound function block AssertionalSimulation Parameterization 1102.

Reference Data Models are created, assembled, initialized, modulated,manipulated and otherwise operated upon by users and system processes,and are used broadly throughout a Platform 1000. These aggregations ofinformation (and sub-sections of these aggregations) are directly andindirectly employed, accessed and otherwise referenced by and withinmultiple areas within a DTS Platform 1000, and may be utilized invarious forms, in various stages of development and parameterization aswell as in various configurations and modalities (as outlined in thisdisclosure). In this sense, Reference Data Models and various supportiveand ancillary functions may be considered a central aspect within theoverall functionality provided by embodiments of a DTS Platform 1000,involved in a variety of operations that include (but are not limitedto): parameterization and execution of various types and modalities ofAssertional Simulation and related activities; user- and systemprocess-driven activities throughout the DTS competitive andcollaborative environment; parameterization, formatting and execution ofvarieties of analysis and visualization of data directly and indirectlygermane to Reference Data Models using DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures; metering and analysis ofuser- and system-engendered activity including functionality related toDTS in-app user- and system-performance and feature utilization,activity profiling and resource access measurement (as cited, forexample, in FIG. 1 in relation to data/control flow In-app Metrics/Logs1110 and as described in this disclosure).

Reference Data Models, therefore, occupy a central place in manyoperational facets throughout a DTS system, and across these modalities,ancillary and supportive methods and procedures and interfaces may bedeployed by users and system processes to enhance and extend its utilityand application. Thus, for example, a Platform 1000 may provide an arrayof DTS-based (and/or DTS-enabled and/or DTS-accessible) tools andprocedures enabling analysis, measurement and statistical andmathematical processing as well as graphical visualization techniques tobe applied to and upon Reference Data Models (and other such edifices)in a variety of DTS operational contexts. Among these capabilities aremethods and computational techniques adapted from data mining, businessanalytics and performance evaluation and measurement systems but whichin embodiments of a Platform 1000, may be additionally (or instead) beaugmented, supplemented and contextualized within DTS-based and/orDTS-enabled and/or DTS-accessible methods and procedures and interfaces.Thus, in embodiments, a Platform 1000 may employ a spectrum of methodsand procedures and interfaces providing or facilitating these types ofdiverse and specialized analytic tools, technologies drawn and adaptedfrom a variety of industries and applications that may, in context, beapplied directly or indirectly, in part or in full to the range ofoperations related to and supporting the life-cycle of Reference DataModels (as well as to informatic entities (such as, for example,Assertion Models, Apportionment sub-Models and Outcome Models andassociated ancillary entities and other informational structures) butalso to related ancillary and support operations, where such diverseanalytic procedures may optionally and contextually be reconfigured andredeployed, recast and repurposed within the operating context of aPlatform 1000.

Thus, DTS Platforms 1000 may incorporate, utilize or otherwise makeavailable to methods and procedures and interfaces that utilize orconstruct or otherwise operate upon Reference Data Models computationtechniques that may include (but may not be limited to): (i)initialization, parameterization, manipulation and execution of analyticand visualization techniques and related mathematical and logicalprecursory processes; (ii) recursive and looped optimizations and othertransformative operations (as illustrated within but not limited tothose modalities cited in the foregoing); (iii) machine learningtechnologies (in a variety of modalities including (but not limited to)supervised and unsupervised training, reward-based optimization that maybe based upon or which may utilize pattern detection and trainingcapabilities derived from deployment of various types and configurationsof neural nets and similar techniques; (iv) content-emergent evaluationand normalization techniques as well as forms of reward-basedoptimization and content-derived goal seeking; (v) pattern detection andrecognition and feature map extraction technologies; and (vi)heuristics-based expert algorithms that may be used for datamanipulation, transformation, filtering, optimization and/or management.

Note, however that, while the process and execution flows andfunctionality depicted in FIG. 5 and the descriptions andexemplification of capabilities and operations described in the previousparagraphs and subsequent descriptions may have been centered uponReference Data Models, in embodiments, the same functionality andprocess and data flows may also, without limitation, be applied to otherDTS-based (and/or DTS-enabled and/or DTS-accessible) informatic entities(such as, for example, Assertion Models, Apportionment sub-Models andOutcome Models and associated ancillary entities and other informationalstructures). Thus, unless otherwise noted, it may be assumed that thatany or all of the methods and procedures described here may also applyin other contexts and to other informatic entities.

FIG. 5 and related figures and descriptions describe methods andprocedures centered about the creation, assembly, modification and othertransformational operations that, in embodiments, may be routinelyexecuted by users and system processes upon primordial (or existing)Reference Data Models which may be in various states of assembly anddevelopment, including full completion. Note therefore, that while thefunctionality and example methods and process and execution flows inprevious and subsequent paragraphs and figures may encompass an exampleof the potential life-cycle of a Reference Data Model, such cycles maynot, in all cases and implementations, be executed in linear, step-wiseoperations resulting, for example, in a direct-line creation of acompleted instance. In embodiments, DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces within a Platform1000 may access, extract and operate upon any section or sub-section ofany Reference Data Model in any state of development (as my be permittedby APM-mediated ownership, as described in the foregoing, and as subjectto RAMS access mediation and other access considerations) and mayinterpolate into any of the depicted process flows to accomplish one ormore tasks. Examples of such DTS-provided capabilities include (but arenot limited to): i) DTS-engendered clone-and-modify and permutationalcapabilities (where, for example, one or more aspects of a ReferenceData Model containing financial and accounting information for a givenperiod may be replicated and elements manually or programmaticallychanged, thus creating a new instance of that Reference Data Model uponwhich one or more Assertional Simulations may be run, a capabilitydescribed and documented in various contexts in this disclosure and inconcrete implementations of one or more components of a DTS Platform1000 and as depicted in blocks 1306 and 1406 in FIGS. 3 and 4,respectively); and ii) DTS-provided competitive/collaborativefunctionality (as shown in FIG. 1) subsumed within example compositefunctional block Manage DTS Competitive and Collaborative SimulationEnvironment with Optimization 1117 (where, for example, some methods andprocedures and interfaces enable a variety of modes for selection ofCardinal Models from Candidate Models). Details and descriptions of theprocess and execution flows depicted in these figures, as well asdiscussion of the functional and execution-level aspects are detailed inthe following paragraphs.

FIG. 5 also highlights a novel teaching advanced within the presentinvention, one which represents an extension to and improvement uponcurrent methods related to the exchange and processing of informationbetween disparate systems. This contribution to the art is generallyapplicable to many non-DTS systems and operations and, in embodiments ofPlatform 1000, is broadly present in DTS-based (and/or DTS-enabledand/or DTS-accessible) components and has particular relevance withinone or more aspects of the computational chain of execution in certainmodalities of Assertional Simulation and related activities.Specifically, certain types of information that are processed andoperated upon within a DTS Platform 1000 can be explicitly disconnected(or, in DTS terminology, “de-referenced”) from its native ontologicalreference frames and consequent semantical underpinnings, stripping fromthe subject information both the general and specific informatic contextwhich had provided its definitional and relational unity. In somecontexts, and some modalities, DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces may optionallypermit this informatic disconnection to be reversed (or, in DTSterminology. the information may be “re-referenced”). The elementswithin a subject information aggregate that may be disconnected from itssource context include (but are not comprehensively described by): i)the semantical definitions (as framed by one or more ontologicalreference frames) which are contextually applied to (and in some cases,inherent within) individual elements; and ii) relations between elementsreposed both within and outside the aggregation. Thus, DTS-engendereddisconnection may be applied contextually to semantical relations thatprovide both contextual “meaning” (and degrees of meaning) for anelement as a standalone informatic unit but which also define andcircumscribe the nature and degree of any connective relationshipsbetween one or more such discrete defined elements, including instanceswhere relationships may exist between defined aspects within the subjectinformation and aspects within information external to the subjectinformation. In embodiments, methods and procedures and interfaceswithin a DTS Platform 1000 (and/or DTS-enabled and/or DTS-accessiblecapabilities) provide instances of computational processes by means ofwhich the lexicon and/or grammar and/or structural rules within all orpart the subject information may be systematically and selectivelysubjected to this disconnection and may thereby be fully or partiallyre-defined and recast (contextually and, in instances, temporarily)within one or more new DTS-based frames of reference. This newontological frame(s) provides the basis for the semantical referenceupon which certain methods and procedures and interfaces may thenoperate upon this fully or partially recast information, a basis fromwhich the resultant computational product (and/or any of itsconstituents) may also be optionally re-referenced or re-connected backto the original or native frames of reference.

The coerced disconnection and re-definition of the semantic andcontextual interconnection within and between nodes of information iscalled in DTS terminology “semantic de-referencing”. DTS semanticde-referencing reflects a practical and tangible application of thesynthesis of elements drawn from information science with principles andtechniques drawn from disparate mathematical disciplines not typicallyemployed in these contexts such as, for example, Category andRepresentation Theory. Without foreclosing general applicability toother systems, the theoretical and practical application of DTS semanticde-referencing and the concrete consequences of its application servesboth as a predicate enabling execution of some methods and proceduresand interfaces within a Platform 1000 (especially but not exclusivelythose involved in Assertional Simulation), but also provides acomputational and execution framework defining those and otherinformation-centric computational, organizational and analyticoperations.

DTS semantic de-referencing directly and comprehensively addressesincreasingly common and complex problems that arise when informationmanagement systems operate upon information drawn from disparateinformatic domains. DTS semantic de-referencing introduces acomprehensive set of solutions to solve these problems in a manner priorsystems and sub-systems cannot or do not, providing a suite of methodsand procedures and interfaces widely applicable in many operationalcontexts. In general, information management systems of this type may befound in many types of systems but are commonly encountered in thebroadly-defined Business Intelligence and Analytics (BI) arena wherecomponents that process information to and from data sources are oftencollected within the sometimes-amorphic group of operations called ETL(Extract, Transform and Load). ETL is a common term applied across manydata processing platforms of all varieties, and each system appliesdifferent specific meanings, at least with respect to the goals of thesystem and the methods that implement the processing. But note that thevariety, breadth and complexity of information sources has been andcontinues to increase rapidly and this expansion has engendered aconcomitant surge in the incidence of disjointedness between informationsets that may now be obtained from growingly disparate sources anddomains. This increased “unconnected-ness” within and betweeninformation sets limits the types of computational and analyticoperations that may be effectively performed on such ontologicallydisjoint information sets and current ETL systems using traditional,established methods have not kept pace with these demands topre-condition the information for such downstream processing. Thisfailure is partly due to the fact that the problem-domain addressed bytraditional ETL subsystems, in general, differs from that addressed byDTS semantic de-referencing. In many cases, it is not possible or evendesirable for an ETL system to coerce such informatic disconnection.This may be due not only to the nature and goals of the computationaloperations that an ETL subsystem supports but also because of theinherent complexity of the ontological underpinnings that define andcircumscribe aggregates of information. Even simple aggregates ofinformation are defined by detailed semantical subtleties andinterconnections which are defined by domain-bound and domain-definedframe(s) of reference, ontological foundations which provide definitionand delimitation for the meanings attached to or implied by elementswithin one or more source lexicons (vocabularies) and which posit rulesgoverning lexical usage (grammar), deep, multi-layered and highlyinterconnected semantical schemata that provide a foundation upon whichinformation may be operated upon and understood.

These definitional aspects of information are only a few of the elementsthat may be addressed and manipulated in the importation of informationfrom disparate sources and domains, and methods and procedures andinterfaces within DTS-based (and/or DTS-enabled and/or DTS-accessible)that enable, facilitate and control DTS semantic de-referencing providesystematic means to do so. Thus, current ETL systems and methods do notprovide systematic capability to selectively disconnect elements andproperties within such source information from the original ontologicalframes of reference that underpin, define and circumscribe the relevantinternal and semantical definitions. One consequence is that in currentenvironments, certain types of operations upon disjoint data may not bepossible or, to the degree that such computations may be executed bydownstream processes, ETL systems must be revised with often ad hoc,domain-specific pre-processing methods that are unsystematic andnon-generalizable to other information sets and which, in manyinstances, do not fully address the full depth and breadth of thesemantical skein that suffuses even simple data. Such compromises resultin a variety of problems in other systems, including, for example, alinear increase in programmatic complexity as the variety, complexityand disjointedness of various data sets increases, but in such caseswhere incomplete solutions have been applied, there is often anattendant decrease in accuracy and precision of the results produced bythe applicable downstream operations.

The methods and procedures and interfaces composing and related to DTSsemantic de-referencing, therefore, represent a novel addition to theart serving the broad purposes addressed in Business Intelligence andother information-centric systems, including ETL and the attendantanalytic subsystems. In embodiments, this granular capability, asoutlined and described in the following paragraphs and figures,comprises a unique extension and improvement to existing methods, a newsuite of capabilities that may be operated independently and/orselectively as an adjunct to such prior methods.

Note, however that one way that DTS semantic de-referencing differs fromexisting technologies is that non-DTS systems do not, for many reasons,currently require the same degree of harmonization of informaticdisjunction that may exist within and between elements composed withininformation sets which they access, extract and upon which they operate.That is, other systems that manage information transfer do not require(or in some instances can ignore) such Shannon entropic discontinuitiesas it is expressed and deconstructed in the ontology-based approachwithin the methods and procedures and interfaces forming DTS semanticde-referencing. This difference is in part due to the nature of thedownstream processing executed by DTS Platforms 1000 and other systems:in prior systems and in earlier, less variegated data-intensiveenvironments, there has been no need to integrate the complexities of abroad suite of capabilities that manipulate the deep intricaciesinvolved in management of semantic disharmony since such partial orcomplete informatic discontinuities either do not matter for theoperations to be performed downstream or the disjunction itself isinherent within, required by or ignored by the operations to beexecuted.

In one example of these differences in the required capabilities betweenthe tasks in a Platform 1000 and other non-DTS systems (and theattendant ETL pre-processing subsystems), DTS semantic de-referencingprovides both a comprehensive, systematized suite of capabilities andmechanisms that, in certain DTS modalities, frame and enable one or moreelements within the core functionality of DTS Assertional Simulation,functionality that is itself unique to DTS. This distinction is mostevident in one execution mode of Assertional Simulation which coerces amathematical composition upon and between otherwise (fully or partially)unconnected information aggregates, where this disjunction may exist,for example, in degrees, in different modalities and contexts but suchthat, to the degree that such Shannon entropic discontinuity exists, allor part of the respective aggregates may be fully or partially disjoint,DTS semantic de-referencing provides a novel combination ofdynamically-invoked, content-responsive and context-sensitive methodsand procedures and interfaces that may be contextually combined toproduce meaningful and accurate (that is, Turing-complete) resultsacross a wide spectrum of data types, formats and from many differentdomains. This distinction is also evident in some modalities ofAssertional Simulation (and in some parts of its execution chain), whichmay optionally execute this mathematical functional composition bycomposing (or, as cited in the foregoing, by invoking a type ofnode-by-node DTS informatic convolution as defined previously) one ormore elements within an Assertion-Apportionment pair upon one or moreelements within a Reference Data Model, producing, as described, ajointly derivative Outcome Model. In some modalities and in certaincommonly encountered contexts, however, one or more elements composedwithin information constituting the Assertion-Apportionment pair has noobjective ontological connection to one or more elements composed withinthe Reference Data Model. Moreover, as described in the followingparagraphs, to the degree that an objective relation may exist (in somefashion), the relation may have degrees and intensity (as well as othermodulating factors) and such connections may change in differentcontexts. In DTS terms, such disparity describes information sourcesthat are derived from and which are defined fully or partially withinpartially or fully disjoint domains.

In information transfer cases that current (non-DTS) systems manage,these types of adaptive capabilities that ultimately provide the meansto harmonize disjoint informatic aggregates are either not required ormay be required but are not addressed (thus potentially yieldingless-than-optimal results) or may be required and present but which areimplemented in a limited non-systematic way and thus, in currentsystems, comparable mechanisms are not available as independentcomputational capabilities. But note that while many tasks andcomputational and operational contexts executed by and within a DTSPlatform 1000 (and/or by adjunctive DTS-enabled and/or DTS-accessiblesystems) may likewise execute operations that do not require the broadand systematic application of one or more elements within DTS semanticde-referencing capabilities, many of the core and distinguishingfunctions within DTS (namely, certain modalities of AssertionalSimulation and other elements within its ancillary and supportoperations) indeed benefit from these capabilities. Thus, any or all theDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces constituting DTS semantic de-referencingcapabilities may, in embodiments, coexist with any combination of otherexisting and prior ETL capabilities, a wide array of capabilities thatshould be understood here as constituting non-DTS semanticde-referencing capabilities, as described. This is possible for manyreasons, but at least because DTS semantic de-referencing and ETL aredesigned with different outcomes in mind and produce vastly differentresults; therefore, DTS semantic de-referencing should be understoodbeing capable of operating as a novel extension of the broadly definedexisting ETL approaches, one that is adjunctive to such functionality.

One example of such a functional composition of disparate informationmay be seen in even the most basic Assertional Simulation operationwhere the Assertion Model may be constructed using a lexicon that isbased upon meanings that are not present or defined in the ReferenceData Model. In modalities of Assertional Simulation, such disjointednessbetween Assertional and Reference frames of reference are common but ingeneral (but not exclusively) define the Assertion Model coercivelyprojected upon the Reference Data Model. In embodiments, a real-worldexample may be seen in handling accounting data where, in an aggregationof sourced accounting information, groups of accounting entries aredelimited within and separated into, for example, calendar monthlybuckets, a typical and often compulsory accounting time-based constructthat supports conventional calendar month-to-month reporting. Thiscalendar monthly grouping provides a form of temporal definition to theaccounting data in the accounting system domain. Through DTS semanticde-referencing, allocation of the accounting data over a six-weekperiod, a concept not inherently present in the month-based periodicitywithin accounting domain, may be coerced independently of anycalendar-based time events that may exist or may be mandatory in thenative (that is, the accounting) domain. This simple example is meant toconvey, among other things, that while the data in an accounting domainmay be associated with a given month, by applying DTS semanticde-referencing, the relevant portions of the data from the accountingsystem can be redefined in another time-based understanding, here asix-week period. Also, note in this non-limiting example, that while theaccounting data is de-referenced from its definitional calendar month,some semantic and/or structural aspects of the original data may beretained, such as the notion of which particular data is associated with(e.g., defined by an association with) a calendar week. The ability tosystematically and accurately manage such disparate multiple time basereferences is a native capability and system-provided feature throughouta DTS Platform 1000 and the capability to selectively disconnect thenative time-base and harmonize such dissonance is provided to users (andto system processes) in a variety of scenarios, as in the currentexample, within the execution chain of Assertional Simulation (and itsancillary and supporting operations) and the capabilities inhered withinDTS semantic de-referencing.

But note that time-base and informatic periodicity (irrespective of anyparticular symmetry or even localized eccentric temporal behavior) isoften an important organizational premise within information aggregatescompiled within Reference Data Models, and such properties are derivedfrom the underlying domain-bound, semantically-defined intentionalitythat determines the intervals (and any aperiodicity) of a governing timebase. In the present example, one which has general applicability tomany other cases that arise across the spectrum of informationprocessing, network operation and other computational applications, thedomain of accounting operates upon a strictly defined time base,featuring (typically) monthly, quarterly and yearly intervals whichprovide delimiting definitional inflection points, an accepted temporalarrangement that reflects the intentions and domain-specific goals ofaccounting systems. Yet, in the current scenario (and in many similarcommonly occurring situations), the “ground truth” perception a userwishes to infuse into their Assertion Model (and impose upon theaccounting system-derived Reference Data Model) may not comport with theperiodicity and end-point monthly intervals, as described above. Indeed,extending the example, assume that the user-formulated assertion thatextended over the example six-week interval is supplemented by a seriesof aperiodic sequences of daily, bi-weekly and even longer and evenfewer regular periods, any or all of which may regularly traverseaccounting periods. Examples of such sequences include expensesassociated with preparation for, staging of and return from marketingand promotional events (such as conventions and trade shows) as well asinstigation of special projects that may vary in the intensity of therequired organizational resources over time.

Such common situations are both difficult and infeasible to manage inaccounting systems but also, in general, current ETL capabilities do notadequately or systematically address such temporal complexities. In thepresent example, DTS semantic de-referencing provides both the theoreticbasis and practical mechanisms that execute the disconnection of thedeterminative and immutable definition of time base present inaccounting data (where in its native domain the regimentation ofinformation by time interval is a grammar-based imperative) from theinformation composed within one or more Reference Data Models,permitting the imposition of Assertion-Apportionment pairs upon thenow-de-referenced time-based regimentation such that one or more aspectsof one or more Assertion Models may comport with different time basesand/or which may be aperiodic, stipulating a new framework oftime-correlated changes that, in the native domain of the accountingwould appear as random, sporadic internodal and interaggregateaperiodicity and interval discontinuity but which in traditional ETLwould represent a significant computational challenge. Note that suchtime-base diversity can sometimes be accommodated in accounting systems(and in other information-centric and ETL-based systems) but suchexisting approaches are non-systematic (and sometimes even manual), butin all cases, are implemented at the cost of greater complexity and withan attendant increased risk of inaccuracy.

In contrast, and in a further exemplification of the novel contributionof DTS Assertional Simulation, DTS semantical re-referencing (andancillary and supportive methods and procedures and interfaces) andother Platform 1000 capabilities (such as APM and RAMS functionality)systematically address even far more complex instances of multi-variatetime- and frequency-based disharmony (such as may be encountered in meshcommunication networks, distributed computation environments and certainAI and machine learning applications) are routinely addressed thesystematic application of DTS-based (and/or DTS-enabled and/orDTS-accessible) content-sensitive and content-responsive methods andprocedures and interfaces.

Note, however, that DTS semantic de-referencing is especially useful intime-base eccentricity in general. This may be seen in the fact thattime-base variability may exist in many modalities throughout the arrayof subject information aggregates including within any or all ofthe(non-limiting examples: i) the existence of disjoint periodicity aswell as stochastic aperiodicity within and between informatic aggregatesthat may be assembled within one or more Reference Data Models (whereeach such composition may be understood within the definitions broadercontext of DTS semantic de-referencing); ii) such temporaldysconnectivity as in i) above that may also exist within and betweenelements reposed within any set of Assertion-Apportionment pairs; andiii) between such Assertion-Apportionment pairs and the Reference DataModels, as described in the previous examples. But note that time basevariability may surface by virtue of the user- or system-driveninvocation of Assertional Simulation instances at some arbitrary and(potentially) variable frequency. The timing and of interval of suchexecution points may be controlled by or motivated by or mandated in anynumber of ways and from any number of sources: by users, by both DTS andnon-DTS algorithms, by requirements mandated by external rules or rulesrelated to other systems or by some combination of the foregoing.

But note also that such asynchronicity and temporal variability(including differences between the periodicity of the Reference DataModel Data and the execution frequency of the compilation of theAssertion Model) is available to be leveraged by users, by DTSalgorithms and by adjunctive and supplementary systems (some of whichmay co-exist with the DTS system, some of which may be integral to DTSwhile others may be external), where such user-driven and system-basedfunctionality may execute a wide variety of statistical and mathematicalprocessing, as well as applying such varied procedures as patternrecognition and machine learning techniques. In a simple example,suppose, that a series of Outcome Models are concatenated along atimeline where the rate of occurrence (or equivalently, the duration) ofany Outcome result may range from a maximum frequency F1 and a minimumof F2.

In embodiments, therefore, DTS semantic de-referencing and AssertionalSimulation (and other elements within or accessible to a DTS Platform1000) provide a novel and widely-applicable systematic solution to suchtemporal discontinuities by deploying combinations of methods andprocedures and interfaces described here and which may be inferred asapplicable by those skilled in these matters. As one example among manyof this broad applicability, a variety of signal processing andstatistical routines may be applied to such eccentric timelines toidentify, for example, clusters of unusual variations, analyticprocedures routinely applied in such fields as computer vision, networkoptimization operations and inventory and supply chain management aswell as in machine learning environments required to adapt to time-basedstochastic periodicity. Methods for analysis and processing and solutionintegration based upon information defined by (or which contain) suchirregular, eccentric or peculiar (but generally well-behaved) temporalfunctions are well-known in many theoretical and practical arts, butsuch advanced analysis is extended and enhanced by the application ofDTS information products to such procedures, a novel contribution tothose applications made possible by the unique and integrated systematicfunctionality provided by DTS semantic de-referencing and AssertionalSimulation and ancillary and supportive functionality.

Another example of DTS semantic de-referencing may be seen in thepreviously cited example involving the bibliophile who uses AssertionalSimulation to create a categorization system for his library. Asdescribed in prior sections, in simple and increasingly complexuse-cases, the Assertion Model comprises a structured representation ofthis person's point of view about the nature of the subject matter,where the representation may be expressed as simply as a one-dimensionallist, but which may also be as complex as a multi-leveled taxonomichierarchy. In any case, as explained in the descriptions of theseexamples of a DTS deployment, the bibliophile invokes DTS AssertionalSimulation by first stipulating the Reference Data Model as the list oftitles within the subject collection that (in the simple case) includesbooks but which in the more complex latter examples include otherinformation-laden media such as journals, newspaper clippings andreferences and so on. Then, using methods and procedures and interfaceswithin a Platform 1000, he posits the Assertional Model. But note thateven in the simple case of a books-only Reference Data Model whereinformation within the reference is from one media domain (printed andbound books), the terms within the Assertion Model may not (and likelydo not) share objectively common definition (or domain-bound sharedlexicon, one defined within a common frame of reference defined within ashared ontology that is circumscribed within an enclosing domain). Thatis, there is no objective or mathematical manner in which aclassification term in the Assertion Model may be related to thematerial in the Reference Data Model, even where such terms arenominally similar or even identical. In other terms, the Reference DataModel and Assertion Model occupy two subspaces that may not have anoverlapping or region of intersection.

Thus, in the present example (one that exemplifies many other situationsroutinely addressed by DTS semantic de-referencing), the Assertion Modelmay include a user-chosen term such as “Science Fiction” which has nocompulsory, determinative, objective relationship to any term inReference Data Model, even in the event that such a string occurs withinthe Reference Data and has some internal and/or relational relevance toother identical, similar or implied terms. Such occurrences cannot beequated to those which may occur in a user-nominated Assertion Model,irrespective of any apparent or perceived identity relationship. Thus,the user-supplied list that composes the Assertion Model occupies adifferent sub-space than that spanned by the Reference Data Model, wherethese spaces are definitionally disjoint, having no common overlap.

But note that Reference Data Model in this example is composed of a listof discrete topical resources drawn from many domains, such as, forexample, various areas of natural and social science, mathematics andengineering but also selections from the Western literary canon,including novels, poetry and philosophical works as well as modern worksof fiction and non-fiction. Thus, the creation and imposition of even asimple user-perception-based Assertion Model which imposes a taxonomicparadigm upon this diversity is based upon that user's perceived “groundtruth” as to the best way to impose an order upon this assortment. Inorder to do so, the semantical relationship between the semanticalcontent within and surrounding informatic nodes within these variousworks should be divorced from representation embodied within theReference Data Model since user-nominated (and effectively subjective)terms in the Assertion Model, such as “Science Fiction” or “Statistics”,cannot be guaranteed to have a semantical correspondence in anyconsistently provable way to the semantical composition of the subjectdata. The creation of the Assertion-Apportionment pair coerces aconnection between these disjoint spaces, and the projection of itsjoint composition and structure upon the Reference Data Model usingmethods and procedures and interfaces within the Assertional Simulationexecution chain coerces a new semantical connection, an operationfacilitated, enhanced and improved by application of DTS semanticde-referencing.

Thus, as may be seen in these and other examples, the theoretic andpractical application of DTS semantic de-referencing improves upon priorapproaches by providing the same abilities as other systems but withadditional capability able to uniquely address and manipulate one ormore of the elements that define information. In embodiments, theunderlying theory of DTS semantic de-referencing as well as theDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces which permit its practical application providea full range of solutions to semantical as well as temporaldiscontinuity, and this novel type of informatic coercion permits wideapplication of many elements within a DTS Platform 1000.

Thus, given the novel and complex application and implementation of DTSsemantic de-referencing within methods and procedures and interfacesthroughout a Platform 1000, as discussed in the numerous figures andexplanatory text, note, therefore, that it may be applied to informationthroughout a Platform 1000 in multiple non-exclusionary ways and may beinvoked by users or system processes for any number of reasons,including for example, in the context of procedures intended to optimizeand/or combine disparate information for one or more DTS-based (and/orDTS-enabled and/or DTS-accessible) methods or processes. Such processesmay include (in a non-limiting example) parameterization and/orpreparation of information used or accessed or otherwise referencedwithin any or all the aspects, varieties and modalities of AssertionalSimulation and ancillary and support operations, where the latter mayinclude (but are not limited to) analysis of results, optimizations andpermutations, deconstruction and recombination of combined or compoundinformation, and other data-centric operations within a DTS Platform1000.

These example modalities of DTS semantic de-referencing are distinct,however, from the transformative operations that may be prominent withinand which distinguish existing ETL systems in that such transformationsare often simply translative in that such operations interpret andreformat data values of disparate origin into alternative butmathematically (and semantically) equivalent representations in order toserve as input variables to commonly understood analytic operations suchas, for example, statistical packages that execute correlation,regression and predictive operations (to name but a few), datamining-based pattern detection procedures and so on. But note that whilethese extracted and translated values must, in most cases, be translatedor mathematically transformed into a specific and shared mathematicalformat in order to be computed they nevertheless must retain theirdomain-of-origin “meaning” in order for the analytic operations toprovide the information required by the user or the subject system. Thatis, for example, if a system executes a correlative analysis betweendata set A and data set B (where A and B are from different domains,such as, for example, an inventory system and an accounting system,respectively), while the specific data points might from A and B betranslated (transformed) into a common mathematical format, thecorrelation operation still communicates to the user (or the system) themanner in which A correlates to B, a result that is only meaningful (andaccurate) if and only if A and B retain their respective semanticalrelation to their disparate ontological frames-of-references. Thus,given heterogenous information nodes (such that they originate fromdisparate ontological frames) these transformations do not and cannotexplicitly disconnect the possibly-translated values from the semanticmeaning they carry from their domain-of-origin but may (if necessary)merely transform the mode and form of expression from one state to anequivalent other state in order to be processed in a meaningful andaccurate manner. Even in the event that such transforms may inflict somedegree of insertion loss of Shannon entropy, the precision of theinformation may be diminished, not its semantical meaning in theoriginal frame of reference.

By definition, therefore, both translative and organizationaltransformation operations within ETL operations that usually serve as apre-processing stage for downstream analysis operations executed uponheterogeneous information nodes must maintain the original semanticbasis—the “meaning” or semantic context—of a given information node orset of nodes despite what may be highly accurately re-formatting and/ortranslation of such information. Note, however, that even in non-DTSenvironments where downstream operations that may operate upon post-ETLinformation without the requirement that survival of the semanticdefinition provided by the frames-of-origin of that information, thisinformation cannot be and is not subject to some modalities and certaintypes of properties executed by and within Assertional Simulation andrelated operations, as described here. This is due to the novel basicnature of Assertional Simulation: the content-sensitive andcontent-responsive methods and procedures and interfaces projection ofthe Assertion Model upon a Reference Data Model to produce an OutcomeModel that reflects the coercion of the Assertion upon the ReferenceData Model. Such coercive projection often (but not in all cases) isenabled at least in part through DTS semantic de-referencing, such aswhen one or more of the subject Models are assembled from heterogeneousdata sets.

In addition, in embodiments, the modalities and types of DTS semanticde-referencing may include (but may not be limited to) any combinationof the following: (i) de-referencing by severing (or replacing, as inthe foregoing) one or more aspects of the contextual meaning ofinformation contained within one or more nodes, where in this case,“contextual meaning” refers to not only implicit and explicitdefinitions, connective and correlative relationships and othersemantical bases but to logical functionality that may be subsumedwithin or implied by or linked to the subject information as any of allof the foregoing may be defined by and within the original frame ofreference or ontological frame; (ii) de-referencing by severing (orreplacing, as in the foregoing) one or more connective, relational orcorrelative associations or any other logical interconnections that mayexist between two or more such nodes of information, severing (orreplacing), as well, any contextual meaning (as in the (i) above) thatmay exist or may be implied or otherwise imputed by such relationships,associations or interconnections, whether or not such nodes arethemselves de-referenced as in the (i) above; (iii) de-referencing byexecuting any or all of the above within a single aggregation of datawhere such aggregations are defined within DTS in specific manner anddescribed in the following; (iv) de-referencing by executing any or allaspects of (ii) above between nodes of information within a subjectaggregate and those within one or more other separate aggregates.

While in some modalities, DTS semantic de-referencing may remove (orreplace or modify) one or more relationships and/or dependencies thatmay exist between nodes and groups of nodes of information as may haveexisted in its native domain, these subject nodes are referred to in DTSas reposed within one or more “discrete assemblies or discretecollections or discrete structures”, terms that may be used variouslyand interchangeably both within the present disclosure and in otherDTS-related contexts and in concrete implementations of one or morecomponents of a DTS Platform 1000). But note that these terms may havemultiple and contextually-modulated definitions. Any of these terms mayrefer, for example, to nodes of information that are simply logicallyaddressable as a unit (or series of units) by a means of one or moredefinitional (or definition-inputting) access modalities (such as may beaccomplished, for example, with one or more SQL statements where thespecific syntactical structure of the query that enables access toinformation implicitly defines and circumscribes an instance of acollection of discrete nodes). Such collections of information arelogically contiguous (though, not necessarily physically contiguous oreven physically co-located) by virtue of an encompassing addressability(using SQL queries and the like), a unifying property which (in generalbut not exclusively) includes the capacity to be extracted and assembled(subject, of course, to contextual and security considerations).

Note, therefore, that by virtue of such syntactically-specificstatement(s) that enable addressability (and thus access), suchcollections are thus subsumed within at least one (possibly transient)ontological framework or frame of reference, one defined by and embeddedwithin the lexical composition of the access statement (itsgrammar-determined arrangement of vocabulary-based terms) where thechoice of terms reflects the intentionality of the process. In manycases, this user- and/or system process-driven intentionality (whichdefines the frame of reference for the access query and which requiresgrammar and vocabulary defined within the current domain) is explicitonly as long as the statement exists in an execution flow—that is, itexists only for the period in which the information node are accessed(and presumably gathered)—and thus, in practice, the reference frame maybe understood as transient. Such transience is relevant to some methodsof DTS de-referencing where methods from a Platform submit suchtransient queries to another disjoint domain: such submission are notonly ephemeral (as in the above) but may be submitted “blindly” withoutactually knowing the semantical relevance of the vocabulary-based termsemployed in the query. The deployment of such transient constructswithin a DTS Platform 1000 to access (and possibly acquire) inter-domainand/or inter-system information is another distinguishing aspect DTSsemantic de-referencing.

In other instances, the terms “discrete assemblies, collections orstructures” may also (or instead) refer to one or more nodes ofinformation which are not singly addressable by a series encompassingdefinitional access modalities, but which may nonetheless be obtained orextracted from one or more internal or external sources using DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces. This latter case is distinguished by the fact in someoperational contexts there may not be a single frame of reference thatenables construction of programmatic (or systematic) access andprocurement (by means of one or more SQL statements, for example, as inthe foregoing), and, in some instances, there may be no existing frameof reference at all for some or all such information. In such cases, forwhatever reason programmatic or syntax-based access may not besuccessful, users and or system processes may employ other methods suchas (in a non-limiting example) manual or AI-based (or AI-assisted)identification, labeling and extraction. In all such cases, however, thecumulative activity of employing any mode of addressing, obtaining andpossibly assembling what may be, in instances, unconnected orsemantically disjoint information confers a reference frame upon whatmay be otherwise nominally unconnected (or at least possiblyun-addressable) elements, thereby implicitly permitting suchcompilations to fall within the DTS definition of discrete assemblies,collections or structures.

Another variation of the type of information that may be operated uponby DTS semantic de-referencing methods and procedures and interfacesincludes discrete or even contiguous aggregates of information that arecategorically non-specific, or using DTS terminology, “non-specific,inchoate”. The means of access and the varieties of the disposition ofinformation obtained in this modality include (but may not be limitedto) any or all of the following: the information i) may not unified byeither an inclusive lexical composition of an access statement; ii) tothe degree that such access capability exists, it may not be fullyinclusive, consistent or may be discontinuous across the subjectinformation; iii) may be assembled in an ad hoc manner from multipledomains without an explicit unifying intentionality. Such non-specific,inchoate access to information within a Platform 1000 may or may not betransient (as in the foregoing) but is, in any case, fully or partiallydevoid of specific reference to domain-bound and domain-definedvocabularies of the native domain, and thus to the semantical meaning ofthe subject information within its native domain. This type ofnon-specificity may be addressed by the unique capabilities of thecontent-sensitive and content-responsive methods and procedures andinterfaces subsumed within and associated with and available to DTSsemantic de-referencing.

In this context, note that it is general property throughout methods andprocedures and interfaces within a DTS Platform that the structure ofinformation and the meaning of the information within that structuremay, in some contexts and modalities, be treated as linearlyindependent. Specifically, (in one non-limiting example), thecomputational activities executed within (or associated with)Assertional Simulation (as may be seen, in one non-limiting examplediscussed in the foregoing, where the mathematical composition of anAssertion Model upon a Reference Data Model to produce an OutcomeModel), may operate upon either the structure or the composition ofthese entities independently of the other across the range and degree ofpossible computations even if, in their native state, dependencies (insome aspect) may exist between form and content. This coercion of linearindependence upon the form and structure of the constituent Models ofAssertional Simulation is a variant of DTS semantic de-referencing andone that provides the relevant methods and procedures and interfaceswith a novel and unusual premise but one that has a deep impact upon thecomputational execution of Assertional Simulation and across many typesof DTS-based (and/or DTS-enabled and/or DTS-accessible) operationsincluding (for example), simple computations, reorganizations of bothseparate or connected information nodes and other types oftransformational processes. Thus, in DTS operations, the logical (butnot always the semantic) connection between discrete (andnon-decomposable) information nodes (generally referred to here as“structure”) may, in context, be coerced to be computationally separablefrom and may be treated as independent from the domain and ontologicalframe of reference of the information within a node (referred to asgenerically as “content”). The independence of structure and contentsuffuses operations throughout a DTS Platform 1000 but is most evidentin the execution of Assertional Simulation, its types, variations andmodalities and adjunctive and ancillary operations, a property of aPlatform 1000 that centers the creation and manipulation of ReferenceData Models (but which, as discussed, may also be applied to otherinformatic entities).

There are additional examples that illustrate the utility and, ininstances, the desirability and the requirement for DTS semanticde-referencing of collections of information within a Platform 1000, andin particular, within the execution chain of Assertional Simulation. Onegroup of examples involves the creation of Reference Data Models asdescribed in the present context of the construction of DTS and depictedin FIG. 5. The example process flow throughout FIG. 5 illustrates notonly the tangible real-world realities that underscore the need for DTSsemantic de-referencing, but also provides examples of the execution ofthe disconnection and redefinition of information clusters within and bymeans of methods and procedures and interfaces throughout a Platform1000.

Referencing FIG. 5, note that specific operational aspects of theexample process and execution flows and the elements that compose thefunctional blocks in this example are described in more detail in thefollowing paragraphs, but in the current context highlighting the novelcharacteristics of DTS semantic de-referencing and its prevalence withinand in conjunction with methods and procedures and interfaces throughouta Platform 1000, these and subsequent figures and descriptions provideuseful illustrations. In FIG. 5, an examination of the data flows 1172Cthrough 1172E indicates the breadth and scope of the possible diversityof the information that may be integrated within both existing andprimordial and embryonic Reference Data Models. In embodiments, suchinformation may be obtained and assembled from internal or externalsources as shown by data flows 1172A and 1172B which serve as inputs tofunctional block 1104 discussed in the previous descriptions of FIG. 1.Such information may include (but may not be limited to): (i)information that may be heterogenous, distributed, incomplete andfractured; (ii) information that may be drawn from multiple anddisparate and possibly discordant and disjoint domains; (iii)information which may be configured in any number of structural andcompositional modalities; (iv) information has been defined withinmultiple and/or dissimilar and/or disjoint frames of reference (orontologies).

While details of the methods and procedures and interfaces withinfunctional blocks 1130 and 1180 are described in more detail in thefollowing paragraphs, in the present connection, note that, in thespecific (and non-limiting) example depicted in FIG. 5, functional block1130 may in variations serve as a primary (but not necessarilyexclusive) point of execution of DTS semantic de-referencing: any or allof the types and modalities of DTS semantic disconnection described inthe foregoing may, in embodiments, be executed by one or more methodsand procedures and interfaces within functional block 1130. Note,however, that in the present example, functional block 1180 may alsoprovide and/or execute additional or supplemental functionality that mayassist methods and procedures and interfaces within block 1130 inexecution of DTS semantic de-referencing, where such functionality mayinclude (but may not limited to) application of organizational,transformational and translational computations to subject informationstructures (this interconnection is shown by bi-directional data flow1179B). In embodiments, this contextual or conditional feedback ofinformation is characteristic of a configuration described by theDTS-engendered term “intermediate recasting”, a method of processinginformation with a DTS Platform 1000 that may be related to (but may notin all cases integrated within) looped recursive operations describedelsewhere in this disclosure.

The actual computational processes and order of execution which may beemployed within functional block 1130 may vary depending on theexecution context and the type of information being processed—this isone reason the label on functional block 1130 is phrased Dynamic ElementSelection, Assembly, Transformation & Parameterization of Mixed DataSets. In particular, in this collection of methods and procedures andinterfaces and in other methods and procedures and interfaces executedwithin any DTS-based (and/or DTS-enabled and/or DTS-accessible)executable environment, specific processes and order of execution (andother algorithmic considerations) may be context- and content-sensitiveand responsive. This adaptive invocation and execution is a native anddistinguishing characteristic within and throughout DTS Platforms 1000,as described here and elsewhere in this disclosure, and as implementedwithin various embodiments. Note, as well that the results of operationsexerted by block 1130 to produce the resultant data source, Diverse DataReference Frames 1131, (and as controlled and initialized by means ofintegrated control signal Platform-wide Controls and Interfaces 1111),which result in data flow Selected Data Sets 1179A, discussed below.Note as well, that in the example representation of this functionalitydepicted in FIG. 5, control block Structure, Assemble, Filter,Normalize, Scale & Re-Reference Multi-Domain Data into Hybrid Domain DTSReference Data 1180, discussed through this description (see FIGS. 5, 7,8 and 9, for example), may provide a feedback data source 11179B back tocomposite block 1130.

Thus, in the current context of DTS semantic de-referencing, exampleprocesses that may be dynamically or contextually invoked (implementingone or more types and/or modalities of DTS semantic de-referencing) mayinclude (but may not be limited to): (i) methods and procedures andinterfaces that induce or coerce re-combinations, re-orderings and/orre-labelings upon and within collections of information, upon and withinand between one or more sub-sections within collections of information(where such sub-sections comprise groups of information nodes that maybe further decomposed) and/or upon and within non-decomposable nodeswithin a collection—such that in all cases, the operations severgeometric and textual relationships and interconnections that may havebeen defined within the information by means of and relative to thenative ontology (where examples may include deconstruction of linkedlists, reordering of tree structures and collapse of taxonomicstructures); (ii) transformative mathematical operations executed uponcertain data types (such as vector quantities) or structuralconfigurations (such as matrices and arrays) which may maintain thevalue of the data (at least within its native frame of reference) butwhich removes its contextual and relational connection to otherinformation (that may be defined within or connected to that originalframe of reference), thereby severing or transforming or otherwisechanging the original contextual meaning imputed by the connection(where examples may include transformation of spatial information to adifferent coordinate system but in so doing adding or extendingdimensionality, coercing nodal (or value) independence by removing thelinear relationship one or more nodes (or values) may have with othernodes or values, thereby removing (and implicitly) re-defining themathematical meaning of each node).

Continuing the present example, the dynamically assembled methods andprocedures and interfaces within functional block 1130 produce outputdata flow Diverse Data Reference Frames 1131, a compound data flowconsisting of collections of information that have been de-referencedfrom their original frame of reference within functional block 1130, butwhich, in this example implementation, are reassembled into newcollections of information with different ontological bases (in whichthe collection is assembled upon the basis of its new reference frame)and placed within an array or collection of data stores. Note that thepresent example reveals a variation of the previously cited“intermediate recasting”, a characteristic within variations ofReference Data Model construction (and in similar modalities within aDTS Platform 1000 as well as in modalities invoking DTS semanticde-referencing) wherein results of computations that are subsumed withfunctional block 1180 may be returned back to functional block 1130 aspart of one more operations applying changes to one or more parametersor variables (as, for example, may be executed in an iterative DTSsemantic de-referencing operation) within collections of information(where, in this non-limiting example, such results may have includedinformation or results originating from anywhere in the processes flowdepicted in FIG. 5).

In this example implementation, functional block 1130 outputs data flow1131 which subsumes de-referenced collections of information that havebeen cast, using any or all non-limiting example methods in theforegoing, into one or more new ontological states (as indicated bycallout reference Elements from Ontology A thru Ontology Z 1133) whichdetails components of data flow 1131 as shown within bracketed datastores, 1132A and 1132B). In the absence of further processing (bymeans, for example, of application “intermediate recasting” as describedabove) these newly-minted and de-referenced collections of information(or sub-sections or nodes, as described in the foregoing) may beselected and, in variations, combined with other such information fromother ontologies (some of which may be similarly newly-minted but invariations may also (or instead) be drawn from non-dereferenced sources,a variation of “intermediate recasting”, previously cited). Data flow1179A depicts the results of the dynamic and content-sensitive andcontext-responsive selection of this information by methods andprocedures and interfaces within blocks 1130 (and conjunctively, withinblock 1180), information that may be subjected to further processing butwhich, in any case (in the present example) may then be included in anew (or reformatted or otherwise changed) Candidate Reference DataModel. The elements within this information structure have beende-referenced from its original source (in any or the modalitiesdescribed above) but is now defined within a DTS-engendered ontologicalreference frame, one which is optimized for the particular DTS-based(and/or DTS-enabled and/or DTS-accessible) processes and proceduresselected by the user or system process or required by one or moreoperations within or conditions that may be present in a Platform 1000.

FIGS. 7, 8, 9 and 10 provide non-limiting examples of DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesthat implement process and execution flows for variations of DTSsemantic de-referencing, examples presented without foreclosingalternative implementations. Note, however, that in embodiments, theseexamples may also reference other aspects of DTS functionality and maybe equally germane to any aspect of Assertional Simulation and itsancillary and supplemental operations. These example process andexecution flows further illustrate the differences between DTS semanticde-referencing and existing ETL capabilities and systems.

FIG. 7 illustrates an example of DTS semantic de-referencing methods andprocedures and interfaces deployed to assemble a Reference Data Model,5508 Reference Data Model RDM-1 from heterogeneous information obtainedfrom multiple (possibly disjoint) domains within an enclosing domain5500 Domain Q where domain Q is disjoint to the domain occupied by theDTS environment (5505 Domain D). FIG. 8 expands upon the functionalityimplemented by (and within) example elements that may be deployed in DTSde-referencing operations and illustrates example variations of this DTSnovelty. FIG. 9 provides an example data structure subject to variationsof these operations. Finally, FIG. 10 illustrates how fully- orpartially-de-referenced Reference Data Models maybe used to construct ormodify other elements in the Assertional Simulation chain, includingAssertional Models and Apportionment sub-Models (also referenced asAssertion-Apportionment Model Pairs) and thus used in an AssertionalSimulation to produce a Candidate Outcome Model, 5604 De-referenced DTSCandidate Outcome Model.

Referencing FIG. 7, methods and procedures and various informationstores resident within a Platform 1000 are designated as occupying anenclosing categoric domain Domain D 5505. A second disjoint enclosingdomain Domain Q 5500 spans information contained within and/orreferenced by and/or otherwise accessible to one or more physicalsystems confederated within enclosing domain 5500 Domain Q, where suchsystems may, in some situations, be fully or partially physically and/orlogically disjoint from one another but which may also include one ormore operational components residing within one or more DTS-based(and/or DTS-enabled) systems, any of which may be logically orphysically continuous to the subject Platform 1000, but where in thelatter case, such elements are entirely subsumed within Domain Q 5500and thus definitionally disjoint from Domain D 5505. As shown in FIG. 7,regardless of the physical and logical location (and system context) ofthe information spanned by enclosing domain Domain Q 5500, suchinformation is depicted as residing across multiple possibly (but notnecessarily) disjoint domains as depicted by callout 5501B Other Domainsin Q, a commonly encountered situation.

The current example (and related FIGS. 7-10) centers upon informationwithin a specific domain within enclosing domain Domain Q5500-Multi-Domain Q_(A) 5501A which, in FIG. 7 is depicted as composedof information and associated methods distributed within a collection ofdomains designated as Q_(A1) through Q_(Ak). For illustrative purposes,these elements are shown as subsumed within data stores 5502 Q_(A1) and5503 Q_(Ak,) respectively. Note, of course, that such a one-to-onestorage method is not in any way a physical or logical requirement oreven a functional necessity but rather illustrates an important aspectof the current example: namely, that the information passed to thePlatform 1000 in this example (designated as 5502 Q_(A1) and 5503Q_(Ak)) is obtained from enclosing Domain Q5500 but defined within theenclosed construct Multi-Domain Q_(A) 5501A. Note, as well, that calloutHeterogeneous Data from 1172A-1172E 5512A indicates the wide structuraland compositional variegation of information throughout both enclosingdomain Domain Q 5500 and Multi-Domain Q_(A) 5501A, as described in theforegoing and elsewhere in this disclosure, where this variety is not arequirement but is rather one of a variety of commonly-encounteredbroadening conditions which the functional elements within the currentexample (and the methods and procedures and interfaces provisionedwithin DTS semantic de-referencing capabilities) must accommodate.

In this connection, note that in the current example, Multi-Domain Q_(A)5501A is specified as defined by a reference frame (implicitly orexplicitly) contained within composite functional block Schema for Q_(A)5504A. In this example (and in others), block Schema for Q_(A) 5504A isrepresentative of such blocks that depict a collective functionalityprovided by (possibly diverse) collections of resources which define andcohere one or more elements of the ontological definitions within adomain and which, in these instances, serve as a functional repositoryfor both such organizational information as well as the associatedcomputational components to support other capabilities that may need toaccess such information. Thus, in variations, and in the presentexample, block Schema for Q_(A) 5504A represents a compositefunctionality of DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces as may be required by executingfunctionality associated with DTS semantic de-referencing with known andcommon computational and support elements, subsuming, for example,methods and interfaces as well as informatic mappings, definitionaldocuments and references. In embodiments, example composite block Schemafor Q_(A) 5501A may include or may otherwise reference or access any orall of the following non-limiting example computational and referencefunctional elements: structural and/or compositional schemata defininginformation relevant to the subject domain (in this example case,Multi-Domain Q_(A) 5501A), content- and context-compliant (andcompatible) data dictionaries (which may be fully or partiallytranslated, re-compiled or modified dynamically and in context),glossaries and translation tables and other such interpretive vehicles,as well as methods and procedures and interfaces that may be employed tocreate, maintain and execute any or all of the foregoing organizational,functional and access-enablement information.

In the context of DTS de-referencing (and with respect to datamanagement in general within a Platform 1000), such composite schematastructures (in whatever form they may take in variations both within andoutside of a Platform 1000) serve as discriminatory reference focibetween and within domains, implementing domain-defining (anddomain-bound) mapping resources and information that express,circumscribe and contextualize the semantical and lexical nature of“things” within a domain. Those fluent in these arts will note that thisdefinition is broader than is typically used in database design andimplementation as well as in prior, traditional ETL systems since itspans the information and associated creative and execution capabilitiesapplied to information and representations of information within adomain rather than simply the organization and relationship of tableswithin databases. Rather, in the context of DTS, these schematastructures (such as Schema for Q_(A) 5504A in FIG. 7 and others) shouldbe understood as a broader operational ontological construct, one usedto define, for example, what “things” within a domain mean, how and why“things” are connected to other “things” within (and in some cases,outside) a domain as well as where and why such “things” may be locatedand, in important ways, why certain “things” are so defined and locatedwithin a domain in contrast to other definitions, locations andrelations associated with other “things”.

In concrete terms, in embodiments (including in implementations of aPlatform 1000), as one example of this functionality, Schema for Q_(A)5504A may include (but may not simply be limited to) such standardfunctionality as functions and classes within or referenced by dataaccess-enabling Models (as in a Model-View-Controller (MVC) frameworksuch as .NET), collections of one or more SQL-formatted database tablesdynamically connected using various formatted access modalities (such asvariations of SQL statements either statically contained within ordynamically and transiently assembled within or by one or more Modelwithin an MVC-based system), and one or more instances of referencemapping schemata that may be memorialized in one or more documents orreference or cross tables but which may in any case provide (or providedthe means to compute) the unifying organizational principles andmappings defining Multi-Domain Q_(A) 5501A. But note that such standardfunctionality is augmented, extended and enhanced and in some instancestransformed by the methods and procedures and interfaces as describedhere and which may be functionally associated with DTS semanticde-referencing (and any ancillary operations) and thus, to anyoperations related processing and execution chains within AssertionalSimulation.

In embodiments of a Platform 1000, there may be any number of methodsand procedures and interfaces by means of which information nodes fromsuch disparate sources and domains may be accessed, selected andassembled. In FIG. 7, these industry-standard capabilities are subsumedwithin or otherwise associated with composite functional box ManageInternal User Control, Interfaces and External Data and API Access 1104as depicted within and described in relation to FIG. 5. Thus, note thatPlatform 1000 may include one or more one instances of compositefunctional box 1104 (or its equivalent or a suite of equivalentfunctionality distributed but nonetheless fully or partiallyinter-accessible amongst other such functional blocks), and through thiscollection of methods, in this non-limiting example, a collection ofinformation from 5502 Q_(A1) and 5503 Q_(Ak) is obtained from 5501AMulti-Domain Q_(A) and stored in data store 5506 Q_(1D). Callouts 5500and 5505 and the thick dotted line above block 1104 provide a graphicalreinforcement of the distinction between Domain Q 5500 and Domain D5505, emphasizing again that operations performed within Platform 1000upon information obtained from (in this example) 5500 Domain Q (andspecifically from Multi-Domain Q_(A) 5501A) are explicitly executedwithin a disjoint reference frame defining Domain D 5505, a pointfurther emphasized by callout 5507 De-Reference Q_(1D) from Domain Q toDomain D.

This functionality is illustrated at a high level in FIG. 7 wherecomposite functional box 1104 represents one manner in which theinformation collection within data store 5506 Q_(1D) is de-referenced:by virtue of the fact that no aspect of the organizing informationdefined within composite functional block 5504A Schema for QA nor anymethod of execution of such schemata are included within or referencedby information collection within data store 5506 Q_(1D). Thus, apartfrom the information required by composite block 1104 to access, selectand obtain this collection of information, no other definition required.In embodiments, and as discussed in subsequent discussions of depictionswithin FIGS. 8-10 (as well as in other representations of theseteachings) such “full-de-referencing” may be optionally partially orfully rebuilt, reconstructed or reconstituted within the enclosingdomain spanning one or more elements of the subject Platform 1000. But,as noted, in embodiments, a Platform 1000 may execute de-referencingacross a spectrum, so that in instances, one or more components of itscompositional meaning and context and/or any internal (or evenexternally referenced) structural relationships inhered within (orotherwise referenced by) the subject information may be de-referenced inany combination and reconstituted as and when required for use inDTS-based (and/or DTS-enabled and/or DTS-accessible) computationaloperations, including within any or all of those cited in the foregoingand in other descriptions within this disclosure.

Continuing through the process and execution flow depicted in FIG. 7,the elements within data store Q_(1D) 5506 are, in this example,presented to composite functional block Structure, Assemble, Filter,Normalize, Scale & Re-Reference Multi-Domain Data into Hybrid Domain DTSReference Data 1180 (as described in FIG. 5) and to compound functionalblock Assemble and Parameterize and Select Candidate Reference DataModel(s) 1112 (as described in FIG. 1 and expanded upon within FIG. 5).The functionality represented in block 1104, and blocks 1180 and 1112integrates content-sensitive and content-responsive methods andprocedures and interfaces that may be used to analyze, structure,translate and otherwise operate upon information within data storeQ_(1D) 5506. Note that in this and in other computational contextswithin a Platform 1000, such content-responsive, content-emergent andcontext-sensitive functionality may be invoked within or in conjunctionwith any or all methods and procedures and interfaces DTS-based (and/orDTS-enabled and/or DTS-accessible). This fundamental feature is part ofthe novel adaptivity provided throughout a Platform 1000 and, incontext, refers to the manner in which specific tasks are executed uponinformation depending upon the composition (and in some cases thestructure) of the subject information itself (as well as any otherrequirements determined by or provided as part of a broader system oruser-driven context). A simple example of such content-responsiveness isthe context-based detection of vector quantities in one or more nodes(detecting these values as an ordered list of scalars) so that suchquantities may be processed differently, for example, as scalarquantities.

In the present example, blocks 1104, 1180 and 1112 (and theirconstituent methods and procedures and interfaces) combine to reflectthe broader system and/or user intentionality and requirements entailedin processing the information obtained from Multi-Domain Q_(A) 5501A ascontained within data store Q_(1D) 5506. As mentioned, the specificmethods depend upon both this intentionality and requirement skein butalso considering and responding and adapting to the composition andstructure of the information itself as well as to the nature of thesource domains. In this example, as stated, block 1104 obtains the datafrom data stores Q_(A1) 5502 and Q_(Ak) 5503 drawn from Multi-DomainQ_(A) 5501A, but no elements from Schema for Q_(A) 5504A are includedwithin data store Q_(1D) 5506. Thus, the adaptive and responsiveinterfaces within 1180 and 1112 and content- and content-relevantmethods and procedures and interfaces process this received data toformat, filter, parameterize execute other operations to instantiateReference Data Model RDM-1 5508 (and in implementations, to create,extract or otherwise construct Schema for QRDM-1 5504D). As shown,Reference Data Model RDM-1 5508 is thus not only fully referenced toDomain Q 5500 within this example instance of Platform 1000 but may beused to execute an Assertional Simulation in conjunction withappropriately parameterized Assertion Model AS-1 5509 and Apportionmentsub-Model App-1 5510, structures that, in this example are formedadaptively and responsively by means of content- and context-responsivemethods and procedures and interfaces within Assemble and Parameterizeand Select Candidate Assertion Model(s) 1113 and Assemble andParameterize and Select Candidate Apportionment sub-Model(s) 1114. As inthe foregoing, the constituents Models within Assertional Simulation maythen be presented to composite block Execute AssertionalSimulation—Create and Manage Candidate Outcome Models 1116 and to otherdownstream functionality depicted in FIG. 1 and other drawings anddescriptions.

Note, again, however, the multiple callouts within FIG. 7 that theseoperations are referenced to Domain Q 5500 (see FIG. 7 calloutsDe-Reference Q_(1D) from Domain Q to Domain D 5507 and Referenced toDomain D 5511). These callouts emphasize the fact that, in this example,(as mentioned in the foregoing), the entire semantic frame of reference,the relationship between nodes and the compositional meaning within suchnodes (to the degree that such information may be inferred) was requiredto be extracted and inferred from the information within data storeQ_(1D) 5506 and not from a direct access to or importation from Schemafor Q_(A) 5504A by means of dynamically assembled methods and proceduresand interfaces within (or accessible to) blocks 1180 and 1112. SinceSchema for Q_(A) 5504A was not included in the information provided,this is an example of “full de-referencing”, as mentioned. As mentioned,such full de-referencing and the variations that involve less-than-fullde-referencing provide an important predicate and execution frameworkfor execution of many types of Assertional Simulation.

Continuing the present example, assume that the governing schematawithin Schema for Q_(A) 5504A for the information contained withinMulti-Domain Q_(A) 5501A thus defines, in the manner outlined in theforegoing, both the composition as well as the inter-nodal structurewithin the collection of information drawn from 5502 Q_(A1) and 5503Q_(Ak) and that, in the present example, the structure of the assembledaggregation, as reflected in the relevant schemata within 5504A Schemafor Q_(A) is a taxonomic hierarchy. Note again, however, that thegoverning schema itself is bounded by and defined with the ontologicalframe of reference within enclosing domain Multi-Domain Q_(A) 5501Awhich includes a complex of definitions and relationships as embeddedwithin a governing grammar and vocabulary as well as the methods forcreating, maintaining, organizing, accessing and performing otheroperations upon and with this information. Recall, as well, that theterms “governing grammar and vocabulary” are defined in connection withDTS not in the colloquial sense but rather in the category theoreticsense, and so, in this case, these domain-defining and circumscribingfunctions should be understood as effectively similar: the frame ofreference bounded by enclosing domain Multi-Domain Q_(A) 5501A definesthe semantical meaning of its subsumed (or referenced) information nodeswith terms and rules for the use of collections of terms (grammar orlexicon) where such terms and rules are, in turn, circumscribed by thoseproceeding from enclosing domain Domain Q 5500. In this manner, theorganization of nodes within the collection of information from Q_(A1)5502 and Q_(Ak) 5503 is determined by one or more defining heuristic ordefinitional concepts framed in terms expressed through thedomain-defined vocabulary and grammar and rendered within and executedthrough methods and procedures within Schema for Q_(A) 5504A, asdescribed previously.

Thus, in the present example, assume that there are two modes ofsemantical characterization that apply to the definition of eachinformation node in this hierarchy, aspects that may be nominallyindependent but which are nonetheless related when included in anaggregation: 1) the “internal-standalone” meaning of the node, asdefined by its association with and binding to at least one term in thedomain-bound vocabulary, as in the foregoing; and 2) the“structure-placement” meaning of the node which is defined by the choiceof its relative position within an information aggregate. Therefore, the“meaning” of a node may be derived from any or all of a number ofaspects within these two modes including (but not limited to): i) thedomain-bound definition of the information within each node wherein thedefinition is related to or reflective one or more vocabulary-definedterms external to the structure but where some such terms may makereference to or may have a lexical relation with one or more clusteredgroups of terms which may apply by degree based on context and whichmay, in turn, imply other terms, but which, for the present purposesremain within the domain-bound vocabulary; ii) given i) above and basedupon any or all aspects of the internal definitions, the specific of theplace of this node in the hierarchy; iii) given i) and ii) above, therelation of nodes to one another; and iv) the overall structure of thehierarchy and sections within it. All of these aspects of thecompositional and structural aspects contribute to semantic definitionof a node in this context of this taxonomic aggregation.

In this example, as discussed, assume all the elements comprising thecompositional and structural meaning of a node as described above andthe structure (and the sub-section(s)) within which they are containedbe described and implemented by means of the execution of the governingschema within functional block Schema for Q_(A) 5504A. Thus, in thepresent example, the user- or system-process-defined “reasoning” orintentionality is reflected in the specific choice to include the nodein the structure and its specific placement within a structure, and thispurpose is constituted and reflected within the governing schema withinfunctional block Schema for Q_(A) 5504A and executed by means ofassociated methods and procedures and interfaces.

In this specific example, without foreclosing the applicability of thesemethods to other possible compositional and structural configurations,assume that each node in this taxonomic hierarchy (an aggregationconstructed from the collection of information from Q_(A1) 5502 andQ_(Ak) 5503) is connected to its surrounding nodes by virtue of theschema-defined two-dimensional geometry such that nodes may be connectedby (optionally) possessing a parent-child relationship and/or may beconnected by (optionally) having one or more sibling relationships(where siblings are defined as two or more nodes that share at least oneparent) but such that each node must possess either at least one parentor at least one child. Further, assume that all nodes contain aReal-valued scalar and that no such value may be undefined or null, andthat each node also contains a label (or metadata) independent of itsassociated scalar by means of which it may be identified in one or moreaspects within the Schema for Q_(A) 5504A. In general, this pairing iscalled a key-value structure where a value serves as a unique identifierfor its associated value. But in the current example, let Schema forQ_(A) 5504A stipulate that an index label (or key) is not unique, andthus, the scalar value held within a given node may not, in some cases,be identifiable only by its label, but must be specified conjunctivelyby its relative geometric position. This instance occurs when a label(key) is assigned to more than one node (and thus may refer to more thanone value). In light of this, and in the present example, let thekey-value relationship of a node be called “label/key-value” for clarityand in order to distinguish this case from others where a key is uniqueto its nodal instance.

Continuing the current example, the schema-defined semantical definitionof a node reflects the previously cited compositional and structuralmodalities, and in this context has at least three levels of semanticalrelevance reflecting items i) through iv) in the foregoing: 1) the“meaning” of its label outside the structure as defined by the frame ofreference-bound vocabulary (as in the item (i) in the foregoing); 2) itsconnective relationship to adjacent nodes (its parent(s) and/or itschildren or its siblings) (also as described in the foregoing as well asin as in items ii) and iii)); and 3) its connection to non-adjacentnodes wherein such connection (in this schema) are conveyed bycommonly-shared labels, as in the foregoing items ii) through iv).

Continuing the example process and execution flow shown in FIG. 7, FIG.8 reveals not only more detail from FIG. 7 but shows the additionalvariation wherein several DTS-based schemata (as defined as in theforegoing with respect to Schema for Q_(A) 5504A) are constructed andoperated upon in relation to their respective associated data stores. InFIG. 8, Domain Q 5500 (depicted in FIG. 7 and described in the foregoingand in subsequent example implementations) is shown as having nativeschema Schema for Q 5504Q. The functionality described in FIG. 8 alsodepicts Domain Q 5500 comprising Other Domains in Q 5501B and elementsfrom Domain Q 5500 designated as 5501M Multi-Domain Q_(M) withassociated schema Schema for Q_(M) 5504B. Note, therefore, in the topsection of FIG. 8, that Schema for Q_(A) 5504A (within Domain Q 5500) isshown as providing bi-directional functionality directly within and inconjunction with Multi-Domain Q_(A) 5501A, resulting (among possiblethings not shown in here) in a data store labeled AggregatedInformation—Multi-Domain Q_(A) 5520. This depiction provides arepresentation notwithstanding the many other arrangements that may bepossible since such data stores may indeed be physically and logicallydistributed in any number of ways and may be accessed and administeredby many other components within Domain Q 5500 not shown here. In anycase, data store 5520 as well as the sub-sets Q_(A1) 5502 and Q_(Ak)5503 previously depicted and described, remain, as in those instances,governed by Schema for Q_(A) 5504A. But note that, in this case, block1104 is connected bi-directionally to Schema for Q_(A) 5504A as notedwith the callout Translated Selection Parameters for info withinQ_(A1)-Q_(Ak) 5530. Thus, in this variation, the information in datastores Q_(A1) 5502 and Q_(Ak) 5503 is selected by means of one or moreaccess modalities that are translated to-and-from Domain Q 5500 andDomain D 5505. This variation permits a number of connections to be madebetween the respective domains, including but not limited to and or allof the de-referencing modalities previously cited.

On this basis, it may be evident to those fluent in these matters thatmethods and procedures and interfaces within a Platform 1000 (and thuswithin one or more domains within Domain D 5505) may create one or moreschemata structures as described in Domain Q 5000 in connection withFIG. 7. Thus, in FIG. 8, Schema for Q_(D) 5504C may be generated,created and/or modified or otherwise instantiated in one of two modes orin both interactively, such that the elements composing Schema for Q_(D)5504C may be constructed to or otherwise configured to i) comport withthe contents of obtained data store Q_(1D) 5506; and/or ii) tocondition, reorganize or otherwise modify the contents of data storeQ_(1D) 5506 to comport requirements necessitated by one or more elementswithin (or accessible to) Schema for Q_(D) 5504C. As shown, thisconstruct is bi-directionally connected as well with composite box 1180thereby depicting possible invocation of any or all of the describedDTS-based (and/or DTS-enabled and/or DTS-accessible) context-responsive,content-sensitive and content-responsive methods and procedures andinterfaces as well as any or all looped recursive and iterativeoperations (as represented in composite box 1106 in FIG. 1 and otherfigures). Thus, note that this current example embraces the full gamutof DTS semantic de-referencing since the Schema for Q_(D) 5504C containsno compositional or structural references (as in the first example) orto the degree that such elements are present, exists only in partial andincomplete form, as described in the previous discussions.

A specific instance within this example is depicted in FIG. 9. As shownin FIGS. 7 and 8, compound functional box 1104 assembles informationfrom within Q_(A1) 5502 and Q_(Ak) 5503, (which are extracted from andassembled by enclosing domains by means of Schema for Q_(A) 5504A, asdescribed in the foregoing). In FIG. 8, the callout Translated SelectionParameters for info within Q_(A1)-Q_(Ak) 5530 depicts an implementationwherein compound functional box 1104 may access one or elements withinand/or referenced by Schema for Q_(A) 5504A. As described, there areseveral modalities of DTS semantic de-referencing that may be invoked,including, for example, full de-compositional and de-structuralde-referencing wherein both the external references defining the meaningof a nodal label are de-coupled as well as the inter-nodal connections,but where, in general (but not in all cases), these meanings may berebuilt by means of methods and procedures and interfaces within the newdomain, in the present case, Domain D 5505. In this modality, note againthat the frame of reference and the attendant elements of the ontology(vocabulary and both compositional and structural meanings as describedpreviously), embodied within Schema for QA 5504A are unambiguously notincluded in any manner when compound functional box 1104 retrieves theinformation, other than in the structure of the data itself. Thus,though the access modality invoked by methods and procedures andinterfaces within compound functional box 1104 may, in some cases andmodalities of de-referencing, include one or more references derived orextracted from the governing schema in Schema for QA 5504A, in thecurrent case, such semantic schemata are not conveyed in the syntax ofthe collection of access and selection commands and thus the methods andprocedures and interfaces within 1104 are uninformed beyond the bareminimum of “get information nodes that live within these boundaries”. Inconcrete terms, this may be an SQL statement executed through one ormore API wrappers that simply specifies boundaries but not aterminological-based command defined within the Schema for QA 5504A. Inother modalities, as described, one or more elements subsumed withinSchema for QA 5504A may be included and/or referenced in some mannereither within the delivered information and/or may be included in theaccess and selection modality (such as for example, formatted SQLstatements), thereby supplying one or more translation mechanisms bymeans of which one or more aspects of the external and structural ofmeaning one or more nodes may be transferred into Domain D 5505 fromDomain Q 5500.

The current example illustrated in FIG. 9, however, deals with the firstmodality and thus involves complete decoupling between source domain(s)and Platform 1000, and examples of such an instance are illustrated inFIGS. 8 and 9 which respectively show variations in this modality. Notethat this example is presented without loss of general application ofits principles to other variations using alternative methods which maynot depicted here but which may implied, and further, that those skilledin these arts may see that the principles that inform and parameterizeand otherwise frame methods and procedures and interfaces described inconnection to this example may be applied to other variations and toother modalities of DTS semantic de-referencing.

Thus, in FIG. 9, composite functional box 1104, as described previously,obtains information and embeds the retrieved result in data store Q_(1D)5506. But this information is processed by composite functional block1180 such that content- and context-responsive methods and proceduresand interfaces are, in this example, applied to the information withinQ_(1D) 5506. As shown within the brackets, after such processing, thisinformation may be seen as comprising a collection of nodes which areconnected in a particular fashion such that there are structuralrelationships between nodes but, prior to presentation to block 1180,such relationships are not explicit since these relationships are notspecified in another document—as stated previously, there is no suchancillary document in this case—but nor is there any “linkage”information embedded within the data itself (such as, for example,within information pointing one node to the location of another as in alinked list). Note that such data-containing linkages, while not presentin this case, may be present in other DTS de-referencing modalities, andthe specific aspects of Platform 1000 described here apply without lossof generality to those and other cases, as well.

In the present example, let the nodes be organized by Schema for QA5504A and conveyed to composite function box 1104 as nodes aggregatedwithin a series of rows and columns and that this organization is theonly information about these nodes that is passed into Domain D 5505.But as described previously, this example pertains to a taxonomichierarchy with a few rules regarding key value pairs, and yet thisstructure and attendant rules are not described independently in thetransfer of the information—indeed, this is a part of the modality ofre-referencing explained in this example. Thus, in this instance, thetaxonomic structure and the key-value rules are implicit in thestructure itself and must be discovered by methods and procedures andinterfaces within the Platform 1000 and, as a consequence, will thus bereferenced to Domain D 5505 and not to the domain(s)-of-origin. Inembodiments and in the example depicted in FIG. 9, this aggregation isdiscovered (by context-driven application of adaptive, content-sensitivemethods and procedures and interfaces within (or accessible to) block1180) to be a k-level by 3 tree (as revealed in this drawing by groupcall out 5520 K-Levels wide and the depth of the node N_(0K) 5514branch).

In a similar manner, this decoupling and re-discovery may be extendedfurther as exemplified in this example: assume that a node labeledN_(0A) (node N_(0A) 5513 in the Figure 5502) is organized as alabel/key-value pair (as defined above) and let this node contain avalue of x_(k). Recall, however, that in the current de-referencingmodality, while label/keys are defined by a governing vocabulary andgrammar within the frame of reference circumscribed within the hybriddomain spanning Q_(A1) 5502 and Q_(Ak) 5503 and defined within theschemata Schema for QA 5504A and that, moreover, “N_(0A)” has a specificmeaning in the context of enclosing domain Multi-Domain Q_(A) 5501Awhich is also reflected Schema for QA in 5504A, that meaning isexplicitly not included here. In concrete terms, “N_(0A)” may refer to aspecific object (called, for example, “thing 1A”) but this specificlinkage between “N_(0A)” and “thing A” is confined to the governingSchema for QA 5504A and the attendant vocabularies, and thus “thing 1A”has no explicit objective meaning within 5505 Domain D. But on the samebasis, the external meaning of the scalar value x_(k) (the valueassociated with node N_(0A) 5513) is also undefined, though at least oneelement of its implicit meaning is conveyed by its association with itslabel/key (N_(0A)) and by whatever connective relationships may existbetween node N_(0A) and other co-resident nodes. That is, since scalarvalue x_(k) is only defined in Domain D 5505 as the value attached tonode N_(0A) 551 and the meaning of the label/key N_(0A) is, as stated,is no longer associated with “thing A”, whatever meaning scalar valuex_(k) in Multi-Domain Q_(A) 5501A no longer exists. Thus, while it ispossible that the meaning of x_(k) when associated with label/key N_(0A)refers to some quality of “thing A” (its magnitude, for example), inthis instance, this semantic definition of x_(k) is no longer valid.

Note, however, that the relationship between label/key and an attachedvalue not only survives this “complete” de-referencing modality—thevalue, after all, is ascribed to its label/key in the data itself—butthat to the degree that this relationship (and/or any otherrelationship) within the information aggregate now contained in datastore Q_(1D) 5506) does not survive explicitly, such relationships areimplicit in the data itself and are thus discoverable within the newdomain (Domain D 5505). This content-derived discoverability (executedin the example by methods and procedures and interfaces within orassociated with block 1180) leads to the specific knowledge withinDomain D 5505 that the subject information aggregate is indeed a k×3tree by virtue of the arrangement in columns and rows in data storeQ_(1D) 5506. This implicit information permits methods and proceduresand interfaces within block 1180 (and with, in embodiments, inconjunction with and by means of additional assistance from otherDTS-based (and/or DTS-enabled and/or DTS-accessible) processes and/oruser input) to create the Domain D 5505-based paradigm resulting in thearrangement of the subject aggregation of nodes according to thepreviously stated rules—that is, as a taxonomic arrangement connectingparent and child nodes. This example of the “content-emergent discovery”capabilities within Platform 1000 (as well as other content-drivenconfigurations of this type) represents one concrete exampleimplementation of the mechanisms of DTS semantic de-referencing. This“discovery” of de-referenced correspondent and geometric relationswithin an aggregated structure is one execution element within manymodalities of DTS semantic de-referencing.

Continuing this example (and referring again to FIG. 9), node N_(1A)5515 is depicted as a child of node N_(0A) 5513 and let this nodecontain value x_(m), where the previous descriptions and properties ofnode N_(1A) 5515 apply to node N_(0A) 5513 (and to other nodes depictedwithin FIG. 5502). Thus, let node N_(1B) 5516 containing value x_(n) bea sibling of node N_(1A) 5515 and thus, nodes node N_(1A) 5515 and nodeN_(1B) 5516 share at least one parent node N_(0A) 5513. Once again,since these relations are not explicit in the information obtained forthe subject aggregate from Schema for QA 5504A, this relationship mustbe inferred (or discovered) by application of the example methodsdescribed in the foregoing. As depicted, node N_(1A) 5515 has a child,node N_(1A) ^(a) 5517 which contains value x_(q). Examining this treefurther, however, note that the child of node N_(0K) 5514 is node N_(1A)^(a) 5518 and contains a value x_(m+3). This label/key string is thesame as the child of node N_(1A) 5515 (labeled 5517) but each containsdifferent values, x_(q) and x_(m+1), respectively. Thus, as describedpreviously, label/keys strings in this instance, may be shared and thusmust be further defined by their position in the inferred tree structureand by their relation to other like-named nodes. Likewise, the child ofnode N_(1A) ^(a) 5518 (in turn the child of node N_(0K) 5514) is nodeN_(1B) 5519, a node with a value of x_(n) but that in this case, boththe label/key and the associated values are shared between distinctnodes, distinct, that is, by virtue of their surrounding relations.Thus, in this example, as described, these “lost” relationships arerecovered by the previously described methods and procedures andinterfaces within blocks, permitting invocation of previously describedcomposite block 1112 to produce 5508 Reference Data Model RDM-1.

FIG. 10 expands upon the previous example further illustrating thecontent-emergent “discovery” capabilities provided within the DTS-based(and/or DTS-enabled and/or DTS-accessible) content-sensitive andcontent-responsive methods and procedures and interfaces surrounding andrelated to DTS semantic de-referencing. This non-limiting representationexemplifies process and execution flows that may be invoked withinmodalities of DTS semantic de-referencing and DTS-centric processing ofinformation involved in the creation and maintenance of DTS Models andother Platform 1000 operations that provide direct and indirect supportfor Assertional Simulation as well as other ancillary and supportfunctionalities. The collection of methods which execute such operationsare referred to, in DTS-terminology, as “blind semantic extraction andreconstitution” procedures and may, in embodiments have broaderapplication than in DTS semantic de-referencing as in the presentexample, having additional utility in other analytic aspects throughouta Platform 1000, including, for example, within the computations relatedto in-App activity measurement and metrics. The application of DTS blindsemantic extraction and reconstitution operations in contexts related toDTS semantic de-referencing provides further points of differentiationof the methods and goals and capabilities of DTS semantic de-referencingfrom traditional ETL systems, as well as provides another indication ofthe additional capability it makes possible. These elements underscoreand extends the novel contribution to the art provided by this DTScapability.

As discussed in earlier examples, the “discovery” operations seek toreconstitute all or part of a “foreign” (that is, non-DTS, as describedin the foregoing) semantical and structural schemata embedded within ade-referenced Reference Data Model within a new reference frame usingsuch example techniques as induction-based, content-centric featuremapping as well as other methods typically used to induce informationabout data and data structures in the absence of definitionalinformation as may be found in schemata (such as, from example, Schemafor QA 5504A, as described in previous examples). In such “discovery”operations, one or more aspects of the lexical and grammatical skeinthat defines a de-referenced informatic aggregation (in this case, aReference Data Model) may be accessed, extracted, inferred, interpretedor otherwise obtained and optionally transformed and then employedwithin DTS-resident processes to integrate the de-referenced informaticstructures within the local (and possibly new and possibly transformed)ontological frame. In this example, such embedded definitionalinformation may be assumed to have originated from a non-DTS domain(such as, for example, from Domain Q 5500, though this is not a requiredcondition) and may be obtained through any of the means described inprevious examples and illustrations (noting that in such operations, thetarget aggregation is often acquired in the “inchoate” access modality,as described above) and without any governing schemata (also asdescribed in the foregoing). Note, however, that the execution andprocess flows depicted in FIG. 10 (and as described in the accompanyingtext and examples) may be generalized, as mentioned in earlier contexts,so that any or all of the content-sensitive and content-responsivemethods and procedures and interfaces that may be invoked in such“discovery” processes may seek partial recovery (of any degree) of suchdefining information and thus, this example of semantic extraction andreconstitution may be generally applied to any DTS semanticde-referencing environment.

In such instances, DTS-based (and/or DTS-enabled and/or DTS-accessible)content-sensitive and content-responsive methods and procedures andinterfaces may be employed by user and/or system processes to firstextract one or more such lexical and/or grammar-related and/orstructural informatic elements to then transform, extend, translate,re-define, supplement or otherwise modulate such information in orderedto instantiate such elements within the one or more local ontologicalframes of references bounded by the DTS domain. FIG. 10 exemplifies atype of operation within the various execution modalities of DTSsemantic de-referencing wherein this re-orientation and coercedsemantical re-casting is not merely transformative upon the targetinformatic structure (as described in the foregoing) but which resultsin a number of states and modalities upon the basis of which differentoperations may be executed. Such outcomes include but are not limitedto: i) creation or synthesis of a new ontologically-complete frame ofreference wherein this new reference frame may remain discrete but whichmay (in a non-exhaustive enumeration) either share one or more lexicaland/or grammar characteristics with one or more such frames thatcurrently exist within the Platform 1000 where such pre-existingreference frames may have any etiology, or may be fully unique withrespect to those pre-existing frames, having no common semanticalelements; ii) inclusion of one more elements within one or more existingframes, thus extending or supplementing or transforming thenewly-expanded existing frame; iii) any combination of i) and ii) abovesuch that a new, discrete (and indexed) frame is spawned but one or moreelements are also interwoven in to existing frames. Note that invariations, such semantical information may, in the course of any suchoperation be transformed by any number of means using previouslyreferenced bi-directional transformative operations but may also (and/orinstead) be fully re-defined by number of means. In addition, any or allsuch lexical and grammatical elements in any of the foregoing modalitiesmay also (and/or instead) be extended, enhanced or supplemented withadditional semantical properties, including but not limited to: newdefinitions and shades of meaning; newly posited interrelationships withnewly encountered terms; weakened or transformed relationships with newand existing terms; imposition of conditionals and heuristic structuresacross any or all such elements.

Referencing FIG. 10, this example shows how lexical, grammatical andstructural information may be extracted from an information aggregate toform a new DTS-based frame of reference where the extracted semanticalinformation is coerced upon other (in this instance) DTS-nativeaggregates (noting that the native-DTS origination of the coercedaggregated may not be required in other contexts) In this example, theinformation is extracted from a Reference Data Model from earlierfigures and examples (data store 5508, entitled RDM-1) and coerced uponan Assertion Model and an associated Apportionment sub-model. Invariations, these coerced-upon models may already exist, but in thecurrent example, assume that the operations depicted result in anewly-synthetized new Assertion-Apportionment pair. Moreover, forsimplicity, assume that the semantical elements thus induced form a newdiscrete frame of reference with no common or shared semantical basiswith existing frames.

Composite functional blocks 5600 and 5601 (entitled “Extract AssertionalModel references from RDM-1” and “Extract and modulate Apportionmentsub-Model parameters from RDM-1 5601”, respectively subsume DTS-based(and/or DTS-enabled and/or DTS-accessible) content-sensitive andcontent-responsive methods and procedures and interfaces as described inthe foregoing that permit (possibly looped and recursive) examination ofRDM-1 5508, using context and structure discovery methods to developnow-DTS-based schemata (not shown in FIG. 10 but implied as shown inFIG. 8 and as described there). Note that callout 5602 (“Node references& values may be extracted and included in the Domain D Assertion andApportionment Pair 5502”), summarizes the overall operations here whilecallout 5603 (“Selected Extracted Information 5603”) references thecontrol/data flow from RDM-1 to blocks 5600 and 5601, containing thesemantical information extracted from RDM-1) and the output from block5600 that is presented to block 5601. The previously referencedcomposite functional blocks 1113 and 1114 (cited and described in FIG.1, FIG. 6 and other figures) are depicted as adjacent and thus highlyinterconnected to blocks 5600 and 5601, respectively, though inimplementations, all the methods subsumed in these functional blocks maybe engaged in looped, recursive and highly interlocked computationcycles. As shown by the callout 5512B, (“5512B Information from 1172A,1172D 1172E”) any or all information cited in earlier Model assemblyexamples may also be used by any of the DTS-based (and/or DTS-enabledand/or DTS-accessible) content-sensitive and content-responsive methodsand procedures and interfaces within or accessible to these blocks. Asmay be expected, the end results of such operations are reposed withindata stores 5509 and 5510 (“Assertion Model AS-1 5509” and“Apportionment sub-Model App-1 5510”, respectively) and presented toblock 1116 together with Reference Data Model RDM-1 5508 which executesan Assertional Simulation upon RDM-1 5508 using the extracted andnewly-synthesized Assertion-Apportionment pair, 5509 and 5510, noting,of course, the implied step of parametrizing the Apportionment sub-Model(as may be provided by one or users or system processes). The resultantOutcome Model, shown within data store 5604 (“De-referenced DTSCandidate Outcome Model 5604”) is designated as product of DTS blindsemantic extraction and reconstitution operations by callout 5506(“Outcome Model de-referenced from Domain Q to Domain D 5606”). Note, aswell, that this sequence may be repeated any number of times for manyreasons, including but not limited to operations: that seek to discoverone or more optimizations against one or more inferred or nominatedcriteria as may be executed within looped, recursive operations of anyvariety, as described throughout this disclosure wherein, for example,different inferred schemata may be created and tested and possiblymodified; and/or that test or instantiate results based upon differentparametrizations imputed to Assertion-Apportionment pairs; other suchconditional and non-conditional looped or simple repetitions impelled byuser and/or system inputs. Finally, note that any or all such OutcomeModels, may optionally be made available through the control/data flowdesignated by callout 5605 (“Outcome Model 5604 included in collectionof Candidate Outcome 5605”) to other operations through inclusion in thecollections of possible Outcome Models represented by data store 5607,(“DTS Candidate Outcome Models”).

A simple example of this blind semantic extraction and reconstitution inthe context of DTS semantic de-referencing may be seen in an expansionof an earlier example wherein within accounting-extracted data wasde-referenced from its inherent monthly time base to accommodate theeccentric and irregular non-monthly time base within variousAssertion-Apportionment pairs. In this case, assume the same conditionsas in the earlier example, especially that governing schemata was notconveyed with the information that now composes the Reference DataModel. In the present example, the user wishes to execute AssertionalSimulation to allocate indirect expenses to different profit centers, asdescribed in other examples. But here, upon invoking and parametrizingone or more of the content-sensitive and content-responsive methods andprocedures and interfaces associated with blind semantic extraction andreconstitution operations, the user discovers that in this extractedinformation, say for the hypothetical month of May, a new item hasappeared that was not present in the earlier months. The frame ofreference defining the current Assertion-Apportionment pairs cannotprocess this term as it is currently undefined. In this instance, theuser may invoke a user interface mechanism through which the term may becategorized as another instance of other terms, terms that weresimilarly de-referenced from the native ontology (accounting) and thenintegrated within the new reference frame and imbued with new semanticrelevance (within DTS). The DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces that execute thisoperation (and other instances of blind semantic extraction andreconstitution) requires not only context-responsiveness but the abilityto distinguish new term and relations from those already re-constituted.There are many known ways to accomplish this, including, for example,methods that compile and maintain one or more dictionaries andingestation logs, procedures that may examine newly ingested informationfor such anomalies and may then invoke any ameliorative action.

In addition to the operations and functionality surrounding DTS semanticde-referencing as described in the foregoing, in embodiments and incertain contexts, DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces implement another novelcomputational approach to the construction and manipulation of bothReference Data Models (as well as other informatic entities, asdescribed in the foregoing). These procedures may treat the structureand the composition of nodal elements within a Reference Data Model (andother data edifices) as linearly independent properties of that entity.Note that this unique approach is another variation of coercivemanipulation of information (as previously described and as may beprovisioned and executed by means of methods associated with DTSsemantic de-referencing), but in this case, the coercion manifests inthe fact that there may indeed be a mathematical and logical dependencebetween structure and composition within the subject aggregation, butthat each aspect may be operated upon as if no such dependency exists.Such operations may be executed in certain contexts and may be invokedupon aggregates that are in a transient state within a broader executioncontext.

As a consequence, in the operations executed upon those entities, boththe structure and the domain of nodal elements within a Reference DataModel (that is, the properties reflected in the inter-nodal structureand composition of nodes within a Reference Data Model) and the way inwhich these now-independent properties are combined impacts themodality, variety and execution of Assertional Simulation (and otherancillary operations) in number of ways, in some cases creating optionalmodalities that may be presented to users or system processes. Suchalternatives may reflect potential choices as to one or more aspects ofthe type, variety and modality of an Assertional Simulation but alsocontribute to the selection of the computational means by which one ormore aspects of the selected Assertional Simulation may be executed.

Note, however, that this coercion exists within the broadercontent-responsive, context-sensitive and content-emergent propertiesthat, in embodiments, provide a multi-leveled adaptivity presentthroughout DTS-based (and/or DTS-enabled and/or DTS-accessible methodsand procedures and interfaces within a Platform 1000. Thus, thefunctional and execution blocks and the process and execution flowsdepicted within FIG. 1, FIG. 5 and other figures and descriptionsillustrate non-limiting examples of implementations of both the novelcontent- and context-responsive characteristics native within a DTSPlatform 1000 (as such modalities may apply to various componentservices and functionality) but, in another unique extension to the art,where such adaptivity may also be responsive independently to thecoerced separation of information structure and its composition. Inembodiments, such operations of this broadly-based and versatileadaptivity and responsiveness to both context and content as well assystem- and user-engendered conditions and intentionality fall intoseveral general but non-exclusive categories, including but not limitedto: (i) “content-static” procedures (a DTS-spawned term) where suchmethods (and/or collection of methods) are specifically applicable toprescribed array of structural and/or compositional data types; ii)content-responsive, context-responsive and content-emergent procedures(another DTS-spawned term) of which a few variations include (but arenot limited to): a) methods that may be assembled for execution uponcertain types of content (where, for example, text-based informationincorporated within a larger hybrid aggregate of information may beevaluated or processed with natural language processing techniques,keyword search or with user-elicited parameters); b) methods that may beassembled to operate upon certain types of numeric representations(where certain computation routines may be applied to scalar values andothers applied to vector quantities); c) methods that may be assembledas in a) and b) above to extract information from an informationaggregate and which may then select (using system-driven orsystem-provided and/or user-supplied parameters which may include, innon-limiting examples, system or execution context and/or interpretedintentionality and/or solicited user input where any or all suchparameters may reference, for example, lookup tables and other indiciareflecting such conditions) and invoke content-appropriate methods uponthese extracts (in some instances in combination with other informationconditionally extracted from the same subject aggregate and/or fromother aggregates), and where in some instances, the results may beincorporated within the subject aggregate and/or may be reposed withinother aggregates but which may also provide further parameterization forselection and sequencing of other additionally applied methods (whichmay also be applied to the current aggregate and/or to otheraggregates); methods that may be assembled as in a), b) and c) (in anycombination) to be executed within any or all looped recursiveoperations, as described in the foregoing and in other examples withinthis disclosure.

Thus, in embodiments, this DTS-provisioned content- andcontext-sensitivity and adaptivity is multi-faceted and multi-staged andpresent across the spectrum of methods and procedures and interfaceswithin a Platform 1000 but note that it is most often included ininterfaces attached to and which interconnect execution blocks. Suchfunctionality is present, for example, in the composite blocks 1130 and1180 shown in FIG. 1 and FIG. 5 (and in other figures) but is integralin other execution blocks, as well, so that in embodiments, theseinterfaces may be understood as pervasively available to any executionblock. This integration of these versatile capabilities within thesecomputation structures provide repeatable mechanisms by means of whichone or more elements may contextually adapt to both system- anduser-engendered conditions and intentionality but also to the nature ofthe information being operated upon, so that such considerations as (forexample) the specific type and sequence these of procedural invocationsmay be dynamically adapted to, derived from and parameterized bynumerous mechanisms related not only to system-based parameters andcriteria but also, in context, to the state of the structure and thecontent of information being processed. This pervasive operationaladaptivity has numerous modalities, some conditional, and may, inembodiments, be provisionally and even transiently applied (that is,applied in one instance and not in another and/or within one or morepossibly non-contiguous cycles inside one or more looped recursiveoperations (as in the foregoing) and not in others). Examples of thesemechanisms include (but may not be limited to) any or all of thefollowing operations or combinations of operations where such methodsand procedures and interfaces may contextually and, in instances,transiently adapt to: (i) the structural characteristics (where, in anon-limiting example, certain procedures that parse or decomposeelements within a taxonomic hierarchical structure may be required to beexecuted differently from those applied to a flat or unstructured list):ii) the composition of the subject content (where, for example,application of specific methods may be required depending on thecomposition of the target elements in this decomposition, where, forexample, certain arithmetic operations would be applied to scalar valuebut more complex vector-based computations applied in the instance wherethe subject node is multi-dimensional—or both; iii) conditions in i)and/or ii) above where the selection and application of specific methodsmay depend both on the composition of the target elements as well as itsstructural state after (or during) a decomposition operation (as in i)above; conditions in iii) above where the selection and application ofspecific methods applied to one or more aspects of the structural stateafter (or during) operations that operate upon the composition of thetarget elements. In addition, in embodiments, methods and procedures andinterfaces may be contextually and, in instances, transiently and/orconditionally responsive and adaptive to numerous system-basedcircumstances (such as, for example, as may occur in the context of arecursive operation upon a suite of nodes within a Reference Data Modelssuch that certain procedures may be applied optionally and conditionallyat either user or system option, or both).

One example of this combination of content-responsiveness and coercedstructural and compositional dichotomy may be seen in the depiction ofthe methods and procedures and interfaces in FIG. 5 and FIG. 6. Thesefigures illustrate example mechanisms related to the creation (andmaintenance) of Reference Data Models and Assertion Models/Apportionmentsub-Models, respectively, but, as in previous descriptions, such methodsand variations may also be employed in other operations and in relationto other information aggregates. As shown in the upper portion of FIG. 5(and as discussed previously and throughout this disclosure), ReferenceData Models (and other aggregates) may (without limit) incorporateinformation drawn from many different and often disparate sources anddomains and, in embodiments and in some modalities, may also include,incorporate, integrate, combine or otherwise utilize or reference user-and/or system-generated information. The extensive scope of thesesources and versatile and dynamic modes of creation and integration is anovel feature within a DTS Platform 1000, and these capabilities areexecuted by a suite of transformational capabilities integral to thewide-ranging applications of Assertional Simulation and related,derivative and ancillary activities. Thus, the content-sensitive andcontent-responsive methods and procedures and interfaces within a DTSPlatform 1000 systematically and dynamically address potentiallyproblematic heterogeneity by integrating and exploiting this diversity,an approach that exemplifies how DTS leverages such challenges withnovel combinations of computational components to enable and enhance andextend the varied forms and applications of Assertional Simulation andrelated, derivative and ancillary activities. FIGS. 5 and 6 (andsubsequent figures and associated descriptions) provides arepresentative example of this systemization through depiction ofDTS-based (and/or DTS-enabled and/or DTS-accessible) operations that, inembodiments, may be employed to manage and execute creation,manipulation and modification of Reference Data Models.

Reference Data Models may also contain and integrate informationalcomponents defined within a variety of ontological reference frames, maycontain (or be referenced to) information drawn from multiple timebases, may integrate or combine or otherwise utilize (without limit)textual, symbolic, graphical, scalar or multi-dimensional data and maythus exhibit many other types of informational diversity. Further,methods and procedures and interfaces within a DTS Platform 1000 enableusers and system processes (without limit) to create and maintain andpreserve versions, variations and generations of Reference Data Models(and/or discrete elements contained therein) through application ofdiverse combinatorial, evolutionary and permutational operations (asindicated by functional blocks 1309 and 1409 in FIGS. 3 and 4,respectively). In embodiments (deploying, for example, methods andprocedures and interfaces associated with one or more aspects of the APMfabric as described in the foregoing), users and system processes mayaccess, utilize and operate upon any or all such versions and anysubsumed discrete elements.

As shown at the top of FIG. 5, data sources Pre-DTS Internal/ExternalData 1172A and Post-DTS Internal and External Data 1172B refer to dataand information accessible to various functional aspects within aPlatform 1000 (and to the related methods and procedures and interfaces)as well as to Platform 1000 users where such capabilities may, inembodiments, be DTS-based (and/or DTS-enabled and/or DTS-accessible). InDTS terminology, “pre-DTS” reference in annotation 1172A (which also mayappear in other contexts and references and should carry the sameconnotation, unless otherwise) means that information within data store1172A has not been (fully or partially) processed by elements within (oraccessible to) Platform 1000 into one or more DTS-compliant ReferenceData Model structure(s), though, in embodiments, other processing may beor may have been executed. Likewise, the term “internal data” (ascontained, for example, in annotations 1172A and 1172B, but which mayappear in other contexts and references as well with, unless otherwisenoted, the same meaning) should be understood to refer to collections ofinformation (composed in one or more DTS-based (and/or DTS-enabledand/or DTS-accessible) data stores) which are (in general) logically (ifnot physically) co-located within or otherwise natively accessible (andaddressable) within a DTS Platform 1000 (or other executableenvironments accessible to a Platform 1000 (including via one or moredata networks)) where such data may not, in embodiments, always requiredeployment of an external interface. And, of course, external data(within the same annotations) refers to information that is notinternal, as in the foregoing; that is, it refers to data that is notlogically associated with the DTS infrastructure but may nonethelessbecome accessible by means of one or more industry-typical methods suchas API's and other data exchange methods.

The types, structures and varieties of data represented within datastores 1172A and 1172B, and thus available to be processed within one ormore elements within or associated with a Platform 1000, may be diverse,and, in practice, there are few practical limits on inclusion. Note,however, that some information may need additional processing and/orapplication of methods and procedures and interfaces that may not beshown in these illustrations, but which may be assumed without loss ofits applicability to embodiments of the functionality described here oras may be inferred by those skilled in these practices.

The general categories of the structure of data referenced in datasources 1172A and 1172B (as well as in other references and contextswithin or implied by this disclosure) include but are not limited to:structured, unstructured, flat, hierarchical in many forms includingnested subsumptive containment structures, as well as any otherorganizational schemata which may be presented to and in the broadcomputational contexts detailed in this disclosure. Noting that suchreferences to data formats, types and structures within this (and otherfigures) and the accompanying text and other descriptions throughoutthis disclosure are not intended to be exhaustive but rather, to beexplicative of the present teachings, and should not limit their generalapplicability nor foreclose compatibility with alternatives notexplicitly shown.

Thus, with this in mind, note that domains and distinctions betweendomains should be interpreted here in formal information-theoreticterms, such that, in general (but not in all cases), informationsubsumed within a given domain (such as, for example, domains a throughm as defined within ontological frame A referenced in Data from OntologyA 1132A in FIG. 5) should be understood to convey a continuous, discretebut (in variations possibly) divisible ontological space such thatcertain (but not all) qualities within this space, including but limitedto compositional and relational semantics as well as inter-nodal andstructural relationships (however granular and at whatever level ofaggregation) may be defined (implicitly or explicitly) by one or moreorganizing principles (sometimes called a vocabulary in informationtheoretic connections). Such vocabularies (and any consequent structuralrequirements that may inhere from the space, if any) should beunderstood in these informatic contexts as inclusively defined withinthis discrete ontological space and, in this sense, may span all or partof the entire space, noting, however, that such vocabularies, whilerequired, may take many forms and may also themselves be (fully or inpart) implicit and/or may be derived from and/or may be referenced toother (possibly external) structures. Finally, these discrete domainsmay contain mixed data types and various encodings and symbolicrepresentations, optionally including, as well (as in the foregoing),mixed and/or multiple domain information, encodings and other types ofrepresentations.

In this light, it is evident that DTS-eligible data (that is, pre-DTSdata, as described previously) may be drawn and/or assembled from manydomains (or group of domains), including but not limited to (what may bedefined in some reference frames) as data source amalgams and referencesdrawn from non-contiguous as well as integrated sources derived frommixed domain, multi-domain, and single domain information sources aswell as other forms as data source references. Such diversity isreferenced in FIG. 5 as an output of block 1104: Multi-Domain, MixedDomain, Single Domain 1172C, Existing Reference Data Models in SingleDomain, Multi-Domain, Mixed-Domain 1172D and Data Types—Structured,Hierarchical, Unstructured, Flat, Nested Subsumptive Containment 1172E,where, as stated, these designations are not intended as all-inclusive,and noting that in embodiments, any or all such information types andstructures may be integrated within and processed by a Platform 1000 (orcomputational resources available to or accessible to an embodiment ofPlatform 1000). Thus, most generally, the data sources andtype/structure representations shown in FIG. 5 (and in other figures anddescriptions) shown as both input and output to previously referencedcomposite functional box Manage Internal User Control, Interfaces andExternal Data and API Access 1104 may, in embodiments, possess anycomposition (as in the foregoing) where in some instances, diverse (thatis, compositionally different) informational representations anddistinct types of encodings may be combined into a single domain and maybe optionally co-located, internally or externally to a Platform 1000installation. And, as noted previously, to the extent that any suchcomposed information source may not be (explicitly) represented in thepresent illustrations, a Platform 1000 is unambiguously designed (bymeans of interfaces and extensible procedures) to be expandable toencompass (or otherwise access) information in this manner and is thusable (by design and architecture) to ingest, manage, process andotherwise modify such information for inclusion within any constituentprocesses or operations without limitation. After all, new data typesand formats proliferate at a rapid pace, and these illustrations shouldnot in any way constitute or imply limitations to the generalapplicability of these teachings.

Returning to the top section of FIG. 5, the information stores Pre-DTSInternal/External Data 1172A, Post-DTS Internal and External Data 1172Bare made available to the composite block Manage Internal User Control,Interfaces and External Data and API Access 1104, where these inputs mayinclude any of all of the foregoing information types and varieties, inall the cited forms, at any time and in any context and subject to thepreviously described strictures and other requirements withinPlatform-wide Controls and Interfaces 1111.

Note, further, that, as mentioned in the foregoing, Post-DTS Internaland External Data 1172B may also include any internal or external datastore (or section therein) accessible to a Platform 1000 that has,definitionally, been subjected to DTS-based (and/or DTS-enabled and/orDTS-accessible) processing, yielding informatic entities compatible withmethods and procedures and interfaces within a Platform 1000). Suchpreviously-processed informatic entities may include one or moresections within any Candidate or Cardinal Reference Data Models,Assertion Models, Apportionment sub-Model Outcome Models as well as anyancillary or otherwise referenced information (as may be available oraccessible to a Platform 1000) and which may have been created,modified, generated (fully or in part) by one or more users and/orsystem processes. In addition to structures explicitly created by meansof direct user control and/or positive intentionality, any Post-DTSinformatic structures (or parts thereof) may also (and/or instead) bespawned or modified fully and/or partially as the result of applicationof one or more aspects of the previously-referenced looped refinementfunctionality (as described in connection with and as may be related toor enabled by or subsumed within block Manage Looped, Iterative andRecursive Operations 1105, an optionally pervasive functionality that,in embodiments, may be made available throughout a Platform 1000 whichis shown in FIG. 5 as embedded within and made available by means ofcontrol signal/data flow Optimization Control 1108B within Platform-wideControls and Interfaces 1111).

Note, however, that these previously-created (or modified) Models (andsections thereof) would, in general, be subject to constraints andconditions exerted by and made relevant by virtue of the presence of oneor more user-referenced, process-referenced, and data-referenced controlelements that may, in embodiments, be contained within the interfacesand procedures embedded within or associated with both the APM and theRAMS and TCM Control 1109 ownership indexing, access control and eventmediation fabric (as referenced throughout this disclosure).Nevertheless, the present context is illustrative of a core strength andnovelty of DTS Platforms and its constituent elements: its general andpervasive flexibility and adaptability of both Platform 1000 as well asAssertional Simulation in general (including its ancillary andsupplemental and extensible capabilities) but the adaptive andcontextually-conformable, event-driven and context-sensitive nature ofRAMS 1109 and the flexibility inhered in its real-time, instance-basedmediation of (possibly) re-purposed information.

The representation of information described in the foregoing as input toblock 1104 (within stores 1172A and B) and the described representationsof the form and composition of such data depicted as 1172C through1172E) are denoted collectively as example input to composite functionalblock Dynamic Element Selection, Assembly, Transformation &Parameterization of Mixed Data Sets 1130. In general, Block 1130subsumes a variety methods and procedures and interfaces primarily butnot exclusively centered about formatting and interpretative methods andtechniques which individually or in combination may responsively andadaptively interpret and traverse and facilitate (or provide) access tothe previously described variegated (and possibly far-flung) informationstores as described in the foregoing. But note that, as mentioned in theforegoing, any or all of the content-sensitive and content-responsivemethods and procedures and interfaces may be subsumed within orassociated with any execution functionality represented within block1130 (or equivalent blocks)

Thus, block 1130 comprises a collection of methods and procedures andinterfaces within a Platform 1000 that permit users and/or systemprocesses to employ, in embodiments, dynamically and contextually (asdescribed in the foregoing) selectable domain-appropriate computationaland processing methods by means of which these assorted and (potentiallyvaried) inputs may be combined and/or converted or otherwise modifiedinto (possibly) mixed but connected data sets. While structural andcompositional disjointedness is not required nor is a necessarilydeterministically consequence of antecedent processes nor inherent tothe input information stores themselves, in embodiments, such data setsmay indeed compose structural and compositional variety that may beentropically disjoint.

Thus, irrespective of the etiology of the information, of the possiblydiverse (and even nominally) incompatible structure and composition, andeven of constraints that may survive its re-integration as input toPlatform 1000 processes, the collection of methods and procedures andinterfaces within block 1130 provides the means by which users and/orsystem processes may select and assemble such information into nominallyconnected structures and may optionally operate upon such informationthough application of both manual as well as program-assisted or fullyprogrammatically-automated structural and compositional manipulations,interventions that may include but may not be limited to:restructurings, reorganization, recasting, selective and discretescaling and frame transformations, translation, substitutions andadditions of all types. In embodiments, these supplementary andaccretional results may be instantiated with, enabled by and/or assistedor otherwise facilitated by methods and procedures and interfaces thatmay include (but may not be limited to) those that: i) infer informationfrom the content itself and/or from references attached or affiliatedwith the content; ii) those that may instill changes or additions usingrule-based techniques; iii) those that may provide such augmentation asa result application of one or more instances (internal or external)learning and/or content-adaptive operations; iv) those that may bestatistically-assisted and/or which may include or reference othermathematically-based and/or information-theoretic transformations.

In embodiments, the foregoing methods and procedures composed andotherwise associated with block 1130 may (as with many components with aPlatform 1000) integrate or otherwise interoperate with one or moreaspects of the previously-described DTS looped refinement andoptimization functionality (shown in FIG. 5 as embedded within and madeavailable by means of control signal/data flow Optimization Control1108B within Platform-wide Controls and Interfaces 1111). Theseselection, assembly and other operations (including but not limited tothose described in the preceding sections) may employ a variety of suchtechniques including but not limited to: i) looped optimization (as inthe foregoing descriptions and in other sections of this disclosure);ii) comparative and self-modifying routines (as described in theforegoing and in other sections of this disclosure) where such content-and structurally-adaptive processes may include (but may not be limitedto) compositional and structural modification routines (which mayoptionally include but may not limited to self-modifying and/orself-transformational routines) where any or all such methods may bebased (completely or partially) upon parameters that may be obtained bymeans of any or all of the foregoing methods but which may additionallyinclude (but may not be limited to) any or all of the following methodsand techniques): (a) external (in full or in part) to the current data(including parameters and/or operating parameters and/or rules that maybe obtained from local and global data stores); (b) harvested (in fullor in part) from the user input; (c) content-derived, content-adaptive,context-sensitive optionally based upon information that may beassociated with the current data and/or upon and/or which may berelated, referenced by or inferred from to any other data within oraccessible to the system, where such information may be progressivelymodified in successive iterations, but such that any or all suchparameter acquisition may (optionally, at any time in the executionprocess) operate autonomously and/or under full or partial user and/orsystem process control by means of interfaces, methods and proceduresreferenced in the foregoing.

FIG. 5 depicts (without loss of general applicability to otherimplementations) the results of such collective processing (and possiblyrepeated) operations as a composite output data stream Diverse DataReference Frames 1131. This potentially variegated information flow, asmay result from any or all of the foregoing methods and procedures andinterfaces and other enumerated and implied capabilities, includesassembled collections of data elements that may, as described, be drawnfrom diverse and possibly divergent ontological frames as well asdistinct domains within such frames. FIG. 5 provides a graphicalrepresentation of this diversity wherein the reference Elements fromOntology A thru Ontology Z 1133, a data collection reference subsumingrepresentative data sources Data from Ontology A 1132A and Data fromOntology Z 1132B. For clarity, 1132A and 1132B are associate with datastores labeled Data Sources A_(a) . . . A_(m) and Data Sources DataSources Z_(b) . . . Z_(q) where subscripts “a-m” represent domainsdefined within Ontology A and subscripts “b-q” represent domains definedwithin Ontology Z. As those fluent in these arts may see, thisrepresentation provides a non-limiting example of the composition ofoutput information stream Diverse Data Reference Frames 1131. As shown,this data flow is ultimately depicted for convenience as data flowSelected Data Sets 1179A and presented as input to composite functionalblock Structure, Assemble, Filter, Normalize, Scale & Re-ReferenceMulti-Domain Data into Hybrid Domain DTS Reference Data 1180.

Block 1180 (in FIG. 5 and other figures) composes a collection ofmethods and procedures and interfaces that, in embodiments, providemethods by means of which users and system processes may integrate(potentially) diverse data (as described previously) into formats thatare DTS-compatible, and which may be integrated within DTS AssertionalSimulation Simulations and related operations. Thus, assisted byinterfaces and procedures (as may be provided within control signal/dataflow Platform-wide Controls and Interfaces 1111), users (and systemprocesses) may invoke a variety of procedures including (but not in allcases limited to) those by means of which these possibly multi-domaininput data sets within (or referenced by) Selected Data Sets 1179A maybe rationalized, assembled, structured, filtered, normalized to one ormore standard measurements or criteria. In this sense, data sets fromacross the system, including (but not limited to) one or more elementsfrom data stream selected multi-domain data sets 1179A (as previouslydescribed) but also, in embodiments, one or more elements from existingCandidate Reference Data Models 159, existing Cardinal Reference DataModels 1155, as also previously described.

Note that any or all execution modalities and variations related to DTScontent-sensitive and content-responsive methods and procedures andinterfaces described in the foregoing may be incorporated within oraccessed by any of the elements within block 1180 or similar functionalcollections. Further, as mentioned in previous contexts, any or all ofthe methods associated with Block 1180 may be supplemented, augmentedand otherwise integrated with the DTS looped refinement and optimizationtechniques involving any of the data sources and structures describedpreviously, as described previously, such that, in addition to any orall of the recasting operations described as optionally embedded andavailable within Block 1130, additional transformations may be invokedthrough the pervasive available optimization and looped refinementcapabilities outlined in the foregoing. Such supplemental augmentationor refinement may result in deep and fundamental changes to thestructure and composition of subject information such that the netfunctionality produces a derivative new or hybrid domain with newcompositional and structural relationships.

Note further, however that, depending on the specific operations andcontext (as well as the structure and composition of the subject data),the methods and procedures and interfaces within Block 1180 and otherDTS-based data related to and supporting such assembly operations aredesigned and conceived (in general) to provoke as little Shannonentropic loss as possible but rather, methods and procedures andinterfaces involved in such operations (and others implied but not shownor depicted here) are, in embodiments, specifically constructed topreserve Shannon entropy by such (example) techniques whereinontologically disparate and disjoint information may be redefined andre-referenced into one or more new and derivative hybrid domains thuscreating a new but (nonetheless) derivative frame, rather than, forexample, preserving a previous domain by eliminating new or transformedor by altering or otherwise reorienting that information forback-compatibility with an existing domain. In general, these derivativehybrid domains remain related to information from which they emergeddue, in such cases, to a number of factors including but not limited tothe content-centric nature of the (previously cited) re-organizationaltechniques. This ontological conversion should be understood in thecontext of the information-theoretic definitions and their practicalapplication presented in the foregoing such that, in embodiments, thenew reference frame may be synthesized based primarily (but not alwaysexclusively) upon the content itself with (implicit or explicit)definitional structures composed within an (implied or explicit)vocabulary.

The results of such related to Block 1130 is output data stream HybridDomain Data Sets 1178A. These elements may be constructed as describedusing any of all of the foregoing functional blocks and the methods andprocedures and interfaces and other elements composed therein, but, inembodiments, other such techniques may accomplish similar results, asthose skilled in these arts may see. In all cases, any or all elementsthat may be composited within data flow 1178A may be stored by users (orsystem processes) for future use and/or recycling into such stored datarepositories as Post-DTS Internal and External Data 1172B but may alsobe used as input in one or more optimization loops as described in theforegoing.

FIG. 5, however, depicts an example nominal and exemplary workflowwherein data flow Hybrid Domain Data Sets 1178A is presented tocomposite block Select and Compose Reference Data Models 1182, aselection stage subsuming a variety of methods and procedures by meansof which users and processes (through interfaces referenced withincontrol signal Platform-wide Controls and Interfaces 1111). The methodsand procedures and interfaces subsumed within this functional blockproduce DTS-compliant Reference Data Models which in the general caseare referred to within Candidate Reference Data Models 1205 depicted asthe output of Block 1182 and subsequently reposed within eponymouslynamed data store Candidate Reference Data Models 1206.

Note that in the previous statement that output 1205 from Block Selectand Compose Reference Data Models 1182 provides “DTS-compliant ReferenceData Models” but that, in embodiments, there may not be exact structuraland compositional definitions for “DTS-compliance”. These structureshave no single definition but are largely defined by context and may behighly variable.

In any case, however, in a manner consistent with the free-flow of dataand information within a DTS Platform 1000 architecture described here(a transparency subject to one or more access and/or computationalconstraints mediated by elements within and related to RAMS 1109), theconstituents within data flow Hybrid Domain Data Sets 1178A is alsosaved to data store Existing Hybrid Domain Data Sets 1178B and isthereby made available for possible reuse in further steps in a currentprocess or within additional assembly processes initiated and executedas described above (where this informatic structure and its constituentelements are subject, as usual, to the strictures mandated by RAMS andTCM Control 1109). Similarly, data flow Candidate Reference Data Models1205 is both reposed within data store Candidate Reference Data Models1155 but is also returned (or otherwise referenced within) data storePost-DTS Internal and External Data 1172B, again for potential reuse asin the foregoing.

Note that in embodiments of a Platform 1000 and the various forms ofAssertional Simulation, any or all of the methods and procedures andinterfaces depicted within FIG. 5 that relate to the creation andmanipulation of Reference Data Models may also apply in contexts relatedto operations upon and related to any Assertion Model and to anyApportionment sub-Model. In this sense, the depicted arrangement,functionality and interconnection of computational, control and datastorage elements in FIG. 5 (and the related explanatory text) representsbut one possible approach by means of which embodiments of a Platform1000 may be configured to assemble, parametrize and manipulate thestructure and the composition of information embedded within any Models(and any ancillary information) as may be used in and by any of thevarious modes of Assertional Simulation. In this sense, therefore, whileFIG. 5 depicts operations related to Reference Data Models, suchoperations may, without limit and in any context, be applied to otherModels in a Platform 1000.

Thus, returning to FIG. 1, the other functional elements withincomposite block 1102 that focus upon Model creation and manipulation(Block 1113 and Block 1114) may likewise, in embodiments, utilize any orall of the processes depicted and described in connection with theexample Reference Data Model functionality in FIG. 5, noting that suchutility may be applied to those elements without limit (unless otherwisenoted) and without loss of general applicability as a result of suchfunctional and sequential variations and even of additional and/oradjunctive functionality, alternatives and augmentations that may beachievable by those fluent in these and related fields. Therefore,without limit, users and processes may also (and/or instead) applysimilar (and/or additional but related) methods as have been representedand previously described in FIG. 5 to the creation and manipulation ofCandidate Assertion Models and Candidate Apportionment sub-Models andany ancillary structures.

In general, however, Assertion Models and Apportionment sub-Models playdistinctly different roles in various Assertional Simulation modes, andthus, users and system processes may not always use the same collectionof methods and procedures and interfaces depicted in FIGS. 1 and 2 inthe same manner and modalities when processing Assertion Models andApportionment sub-Models as may be (and as may have been) employed foroperations centered about and upon Reference Data Models. In the firstcase, in embodiments, Assertion Models and Apportionment sub-Modelstypically (but not always) evidence less diverse etiology, and thus, theresultant structural and ontological variegation and domain variabilitymay be less prevalent than in some Reference Data Models if notexplicitly more homogeneous.

Moreover, in embodiments and in certain applications of AssertionalSimulation (and ancillary operations), Reference Data Models may (insome cases) tend to be manipulated less often than other Model types,irrespective of complexity and dimensionality, though in general,changes to Reference Data Models may tend (in some cases) to be moreintricate and may tend (again, in some cases) to inhere morecross-domain and hybrid structures and compositions. Thus, inembodiments, and notwithstanding the potential intricacy of theassociated operations (and although the methods and procedures andinterfaces outlined in FIG. 5 (and others that may follow) apply to allModels and affiliated and ancillary elements), certain executableaspects may, in some operational contexts, tend to be more suited toAssertion Models and Apportionment sub-Models, especially certain typesof construction and modification operations. In general, in embodimentsof a Platform 1000, such differences may be at least partially addressedas a UX consideration (that is, as a human factor/functionalitycomprehension issue) as well as a UI problem (that is, as a graphicalissue within a user interface). Note, however, that such seemingpreferential application may be bound with context and instance, butthat such non-limiting statements of modality-related preferentialapplication are by no means definitive, though largely true, and in anycase, certainly do not apply not in all variations within a Platform1000 deployment.

There may be many such commonly applicable but context-sensitiveoperational aspects within Platform 1000, but one illustration of thistendency may be seen in processes associated with assembly of HybridDomain Data Sets 1178A (FIG. 5). While applicable to all Models,deployment of this collected functionality may, in some embodiments andin some contexts, entail less complexity for Assertion Models andApportionment sub-Models than for similar operations related toReference Data Models. While such variability reflects differences inthe respective roles played by each Model in an Assertional Simulation,it also points to the fact that, while elements depicted in FIG. 1 applyto all Models, some functional modes may be more useful to usersspecifically engaged in tasks related the structure and composition ofAssertion Models and Apportionment sub-Models. But note also that thepreviously described content-sensitive and content-responsive methodsand procedures and interfaces which provide dynamic adaptivity in thedisposition, execution and computation of information aggregated in suchModels play a role in execution of this and related functionality, aswell.

Another variation of this varied application of resources is depicted inFIG. 6 (and described in the associated text) and illustrates one mannerin which contextual intentionality may be determinate in deployment andapplication of certain types of methods and procedures and interfaces ina Platform 1000. More specifically, FIG. 6 illustrates the elementsinvolved in an example application of a type of looped refinement (asdescribed in detail in the foregoing), one wherein users and systemprocesses may employ a variety of methods and procedures and interfacesto create and evaluate successive versions Outcome Models based upon asingle Reference Data Model. This is, of course, a commonly-encounteredscenario but this non-limiting example illustrates execution of thisseemingly basic application of Assertional Simulation by deploying acertain type of looped optimization. Forms of this operationalconfiguration are embedded within the broadly-based and pervasivefunctionality depicted within block Manage Looped, Iterative andRecursive Operations 1105 and as disseminated by means of relatedcontrol signal/data flow Optimization Control 1108B in FIG. 1 (andincluded within composite control flow Platform-wide Controls andInterfaces 1111). The operations included within this umbrellacollection of methods may, as noted, entail complex, layered andmulti-faceted computations, but in the present context, aptly qualify asexemplary of certain essential points in the current discussion since,in embodiments, such operations, regardless of potential complexity andirrespective of potential requirement of domain or analytical expertise,may also be executed by non-experts users in more basic operationalmodalities, but moreover, are applicable to all Model types and in manyoperational scenarios.

FIG. 6 illustrates a collection of methods and procedures and interfaceswithin the previously cited optimization operational rubric whichpermits users and system processes to engage one or more loopedrefinement optimization patterns by applying an Assertion-Apportionmentpair (depicted as Assertion Model A^(i) 11005, and (optional)Apportionment sub-Model Aj 11000A) to Reference Data Model 11004 andthen invoking an instance of Assertional Simulation by presenting theseModels as input to composite functional box Execute AssertionalSimulation—Create and Manage Candidate Outcome Models 1116. Thecollection of methods and procedures within 1116 executes AssertionalSimulation upon the subject models and produces as resultant output thedata flow Candidate Outcome Model A^(i) _(,) A^(j) 1201B. Note that dataflow 1201B is the current Outcome Model, where “current” in this contextdenotes that this edifice is the Outcome Model produced and operatedupon within the currently executing cycle within a series of repeated(or looped) operations—though it should be noted that there may, in thelimit, be only one cycle through the loop. Data flow 1201B reflects theresults of the executed version of these particular structural andcompositional configurations upon the Reference Data model,configurations embedded within the constituents of the currentAssertion-Apportionment pair, where this structural and compositionalform is designated as the i^(th) and j^(th) parameter-set applied to andreflected within Assertion Model A and Apportionment sub-Model A,respectively, where i and j represent integers denoting the index of aset of parameters by which means of which each Model (respectively) maybe configured, reconfigured, adjusted or otherwise modified. Thus, uponexecution of an Assertional Simulation by Block 1116 upon Reference DataModel 11004 using Assertion Model A^(i) 11005 and (optional)Apportionment sub-Model Aj 11000A, the consequent output (CandidateOutcome Model A^(i) _(,) A^(j) 1201B) embodies a unique composite andderivative informatic structure which is the direct result of theparameterization of the progenitorial Models, an informatic edificecomposing one potential form of an Outcome Model for this set ofAssertional Simulations.

In the present non-limiting example, FIG. 6 shows that each successiveOutcome Model (Candidate Outcome Model A^(i) _(,) A^(j) 1201B) may beevaluated by methods, procedures and interfaces within functional blockEvaluate Candidate Outcomes 1192, where in this instance, there are twosources for parameter-types that are employed in the evaluative process,where these parameter-types are depicted in FIG. 6 as subsumed withininput data stores Ideal Outcome 1190 and Fidelity Parameters 1191. Asshown in the illustration, the sources for this information are denotedUser and Manual Input 1173 and Internal and External System Input 1174.Inputs 1173 and 1174 may be understood, in embodiments, as related toand/or derived from functional elements composed within compoundfunctional box 1101 in FIG. 1, and in particular, from composite blocksManage Internal User Control, Interfaces and External Data and APIAccess 1104 and Manage Looped, Iterative and Recursive Operations 1105(also in FIG. 1) where the control and data components may beconstituted within one or more elements within control signal User andSystem Control 1108A and Optimization Control 1108B. In embodiments,control signals 1173 and 1174 (and other mechanisms of this type) may bedynamically and/or interactively configured and/or may be preset and/ormay be derived from one or more internal or external control states (orsequence of states) and/or may be derived from and/or inferred basedupon one or more computation resources (which may internal or external,as described in the foregoing), but, in context and in embodiments mayalso result from static and/or interactive user input, where in eithercase, such computational and control elements may actively monitor oneor more elements the form and composition of any of the subject Models,the manner and means by which the looped revision of associatedmodification parameters or any constituent components may be applied orexecuted.

Note however, that while in this simple example, only two basicevaluative parameter fields are depicted, even in a modest illustrativecase, parameter fields 1190 and 1191 may, in embodiments and in certaindomain- and content-related (and other) contexts, contain (or require)potentially complex variables, dynamic content- and context-sensitivedata and other abstracted criteria as well as conditional logic andother variables, data and executables, including (without limit) methodsand procedures that may be adaptive to and/or may be otherwiseresponsive to any number of contextual stimuli including (for example)parameters related to the form and/or content of the subject OutputModel 1201B. Moreover, in embodiments, degrees of computational depth,complexity and variety may be also be embedded within one or morepre-formed routines, where such aggregations may in, implementations, beincluded within a Platform 1000 as groups of one or more pre-configuredfunctional components that may (without limit) be conditionally and/orselectively invoked, edited, disassembled and/or reorganized by one ormore users and/or by system processes, while other instantiations may(without limit) also (and/or instead) permit users (or system processes)to originate and assemble such functionality. Note that, in embodiments,and in context such potential complexities may be invoked or applied orotherwise included for an number of reasons and modalities, includingbut not limited to: (i) permanently required (or conditionallyaccessible) depending upon user and/or system intentionality butpossibly invoked conditionally (fully or in part) due to any reason,including without limit system state; results that may be embeddedwithin (or implied by) one or more loop-dependent parameters, by one ormore content-based or structural characteristics within one of moreModels or related entities; (ii) transiently included within anarbitrarily occurring loop cycle such that the operations are configuredin an otherwise basic modality but which may transition to invokeadditional complexities based upon any or all of the reasons orconditions described in the foregoing but such that the relevantoperations may be applied at user or system process option.

In addition, in embodiments of a Platform 1000, functional box EvaluateCandidate Outcomes 1192 and the related elements depicted in FIG. 6 mayalso (and/or instead) implement, support, access and/or expand to invokemore advanced optimization and related routines (that may be quitesophisticated) where such modalities may not be explicit in FIG. 6 butwhich may, in the context of the potential sophistication ofDTS-embedded looped refined and optimization techniques may beappropriate and possible and, moreover, may be implied as appropriate oras alternatives (as those skilled in this type of optimization may see).In such enhanced contexts, the number and variety of applicableevaluative procedures may integrate, for instance (without limit)multi-variate, time-varying and dimensional parameters and conditionals(and subsume associated calculation mechanisms) and may produce andutilize complex and layered analytic components and dynamic mechanisms,where such variations may include (but may not be limited to): (i)application of such processing and analytic evaluation to and/or betweenone or more current Outcome Models and/or elements therein; (ii) toand/or between one or more earlier iterations of Outcome Models and/orelements therein; (iii) to and/or between one or more current OutcomeModels and/or elements therein and/or between one or more earlieriterations of Outcome Models and/or elements therein; (iv) calculationsand results-engendered modifications to any the aspect of the form orcontent of any of the constituent Models and elements therein, in allthe modes expressed in the foregoing (including within the parametersthemselves), where any calculations entailed within any of the foregoingmodalities may result from computational focus upon only certainelements within and/or between groups of subject Outcome Models andother constituent elements as in the foregoing. Note, however, thatwhile such optional additional complexity has been cited in theforegoing text as related to elements provided by and/or withincomposite block Manage Looped, Iterative and Recursive Operations 1105in FIG. 1, further examples of such operations will be exploredelsewhere in this disclosure.

In the present example in FIG. 6, however, the evaluative processexecuted by functional block Evaluate Candidate Outcomes 1192 consistsof comparing the form and composition composed within Candidate OutcomeModel A^(i) _(,) A^(j) 1201B to a synthetic archetype of its idealized(that is, its desired) form and composition. This archetype is shown ascontained within data store Ideal Outcome 1190, an informatic structurebased upon, received from or otherwise obtained from within one or moreparameters and data elements delivered by means of input signals 1174and 1173 as described previously. Note, however, that this “ideal” maynot in all cases be formalized and stored in this manner but may,instead, exist in the mind or imagination of the user.

The parameters in 1190 are combined with parameters and criteriacontained within data store Fidelity Parameters 1191 which, as in 1190,may be partially or fully initialized by the same input signals as inthe foregoing (1173 and 1174). In this non-limiting example, thesecriteria provide the means by which functional block Evaluate CandidateOutcomes 1192 executes evaluation of the “fitness” of Candidate OutcomeModel A^(i) _(,) A^(j) 1201B in conjunction with conditional blockSufficient Fidelity? 1193. Thus, one or more elements related to theform and/or composition within data store Ideal Outcome 1190 areevaluatively compared with those contained in Candidate Outcome ModelA^(i) _(,) A^(j) 1201B based upon (but always limited to) both thepreviously described criteria on the one side and upon the degree ofconformance to this “ideal” on the other (as obtained from data storeFidelity Parameters 1191 and as supplied by (but not limited to) any orall of the mechanisms described previously). The functional block thatexecutes these measurement (Evaluate Candidate Outcomes 1192) signalsthe corresponding conditional Sufficient Fidelity? 1193 (shown here asseparate though it may indeed be integral) and, if the current OutcomeModel 1201B is adjudged to meet the “best” fit criteria of “ideality”,it is routed to data store Final Outcome Model for Reference Data Modeland Assertion-Apportionment Pair A^(i) _(,) A^(j) 1197, oralternatively, the Candidate Outcome Model A^(i) _(,) A^(j) 1201B ispresented to composite functional block Revise Assertion/ApportionmentValues and Structure 1194. In this latter case, one or both criteriawithin Assertion Model A^(i) and (optional) Apportionment sub-Model Aj11000A may be modified, changed or otherwise manipulated and the processis repeated until a best-fit is found or, in case of direct userinteraction, as shown by control signals Apportionment Changes 1195 andAssertion Changes 1196, until the user (or group of users) are satisfiedwith the Outcome Model 1197 and/or one or more criteria in 1190 and/or1191 are satisfied.

As shown in FIG. 6, whether or not the current Candidate Outcome Model1201B is accepted as sufficiently apposite in form and composition tothe user- or system-supplied “ideal”, that version of 1201B and theassociated Assertion Model A^(i) 11005 and (optional) Apportionmentsub-Model Aj 11000A are routed to and added to both the collection ofCandidate Reference Data Models 1155 and to Post-DTS Internal andExternal Data 1172B.

Continuing with the high-level depiction in FIG. 1, composite blockCompound function block DTS Assertional Simulation Operations 1103 alsogroups high-level representations of the main operational components,methods and procedures and interfaces by means of which a DTS Platform1000 may execute the various aspects of Assertional Simulation and therelated ancillary and enhancement operations applied to and upon theelements composing these Simulations. These operating elements include(in no particular or mandatory order of execution, computationalsequence or precedence) the composite function blocks ExecuteAssertional Simulation—Create and Manage Candidate Outcome Models 1116;Manage DTS Competitive and Collaborative Simulation Environment withOptimization 1117; Select and Store Cardinal and Candidate AssertionalSimulation Outcome Models 1118; and Manage Selection, Formatting,Processing of Models for DTS-based/DTS-enabled Analysis andVisualization 1119. As outlined in previous discussions of thepedagogical intent of FIG. 1 (and as may likewise applied to otherhigh-level figures and depictions), the choices involved incommunication of the particular aggregation and the nomenclatureemployed in the labels of the composite function blocks are non-limitingwith respect to other arrangements and grouping but are nonethelessconsistent with and correctly descriptive of an operational arrangementwithin an embodiment of a DTS Platform 1000. Thus, as in the foregoing,the arrangement within integrated function block 1103 is not the onlypossible combination, aggregation or grouping of the features andcapabilities conveyed by functional blocks 1116, 1117, 1118, and 1119(and the constituent elements within these boxes), and those versed inthese arts may see alternative arrangements which may accomplish thesame or similar operational goals. Further, as in previous (andsubsequent descriptions) the present graphical depiction should notimply specificity or requirements with respect to execution order,sequence, priority, dependence or any other particular computationalarrangement; nor should this depiction imply that any functional blockis required but may, in general, be optional in some embodiments, exceptas otherwise noted.

The first composite functional block within 1103 (Execute AssertionalSimulation—Create and Manage Candidate Outcome Models 1116) subsumes themain methods and procedures and interfaces and other executionfunctionality related to Assertional Simulation. The components thatcompose composite functional block 1116 are expanded and discussed insubsequent descriptions and in accompanying drawings in this disclosure.Note, however, that control signal/data flow Selected Candidate Models1115 depicts the data flow input that the collection of methods andprocedures within 1103 (and the respective subsidiary elements) mayreceive from various Model creation, manipulation and parameterizationstages in 1102 (and in variations and alternate depictions, from othersources). Note further that this depiction should not imply that only asingle Reference Data Model and single Assertion Model (and optionalApportionment sub-Model) may be submitted in a DTS AssertionalSimulation or related execution flow. In variations, and in operationalcontexts, multiple instances of these progenitors may be presentedand/or accessed by 1116 or any other collection of one or more methodsand procedures which may operate upon these elements (which may also becontained in other functional blocks both with 1103 and in aspects ofthe DTS Platform 1000). The specific number of such elements and theorder of execution of any referenced functionality may, in embodiments,be determined and/or optionally inferred by multiple factors, including(but not limited to) any combination (without limitation) of thefollowing: (i) user-mandated requirements as may be presented(non-exclusively) by means of one or more elements within functionalblock 1104 and control flow 1108A; (ii) system-mandated requirements asmay be present or implied or otherwise impelled or conditionallymandated by information or control data that may (fully or in part)originate from within API-related functionality (as shown in functionalblock 1104 and control flow 1108A); (iii) and/or such system-drivenrequirements as may be derived from, as may be pursuant to or as may beotherwise involved with the execution of one or more aspects of subjectAssertional Simulation(s) or otherwise related to information, data orcontrol subsumed with Manage Looped, Iterative and Recursive Operations1105 (as may be required by one or more operational elements to which1108B may provide or receive input from the grouped functionality within1103).

In addition, note that any or all of the informatic structures (andancillary data or references) composed within control signal/data flowSelected Candidate Models 1115 (however constituted, as in theforegoing) may also be presented (or other made available throughembedded or accessible interface structure) to any other operationalblock within a DTS Platform 1000, for any operational and contextualreason and not necessarily as a consequence of any particularcomputation or execution within or related functional blocks that simplyproduce Outcome Models and/or execute Assertional Simulations andrelated outputs, as may be contained with 1116 or any affiliatedfunctions.

One example of such an alternative inter-process flow involves compositeblock Manage DTS Competitive and Collaborative Simulation Environmentwith Optimization 1117. In implementations, this block subsumes acollection of methods, procedures and interfaces primarily (but notexclusively) related to both competitive, collaborative and comparativeoperations but to the various optimization operations, including (butnot limited to) the manual, automated and semi-automated contextsreferenced in foregoing discussions and as outlined throughout thisdisclosure. Thus, Candidate and/or Cardinal Models of all types(Reference Data Model(s), Assertion Model(s) and optional Apportionmentsub-Model(s)) from any generation, from any user and in any format orform may be presented to this collection of functions in this manner(subject to the mediated access control flow as executed by the controlfabric depicted here as RAMS and TCM Control 1109 as may benon-exclusively mandated by Manage Recombinant Access (RAMS) and TopicalCapability Mediation (TCM) (RAMS) 1106).

As may be noted by those familiar with and skilled in these and relatedarts, such interoperative flexibilities wherein capabilities andcomputational tasks and results within workflows may not always besingle-purpose-directed nor simply directed to specific goals, at leastnot always directly. The integration of such workflow elasticity,variegation and combinatorial functionality in the context of theextended analytic capabilities and represent important extensions andenhancements to the relevant arts, but which are routinely providedwithin embodiments of DTS Platform 1000. User and system processes areable to execute many types of operations, in some contexts employing awide variety of analytic and representational tools. Such capabilityillustrates the content-sensitive, content-responsive andcontext-sensitivity built within and fundamental to embodiments ofPlatform 1000, as discussed in the foregoing and as depicted in numerousfigures and descriptions.

Continuing examination of composite block 1103, compound function blockExecute Assertional Simulation—Create and Manage Candidate OutcomeModels 1116 is shown here as both subsuming operations that executeAssertional Simulation upon Candidate Reference, Assertion Models andapportionment sub-Models to produce Candidate Outcome Models, but alsoas containing methods and procedure and interfaces that manage thestorage and disposition of such entities. The choice to combine thesediscrete but related functionalities is mainly a graphical and pedagogicconvenience in FIG. 1, one employed to convey the net activity ofcreation and management and, ultimately storage as embodied withinimplementations of a Platform 1000 and throughout the various modalitieswithin and related Assertional Simulation. Various methods andprocedures and interfaces that may be deployed in a Platform 1000 andothers that may be involved in execution of these respective and other(possibly) related functions are presented and described in otherfigures and the accompanying text.

Given the interoperability between these representative blocks withincomposite block 1103, the primary focus of the functionality and themethods and procedures and interfaces depicted as grouped with groupedwithin functional block 1116 reveal an exemplary but not exhaustivecollection of combinatorial modalities arising from variousconfigurations of Assertional Simulations which ultimately result increation of Outcome Models. Thus, block 1116 is aptly titled ExecuteProjective Assertional Simulation—Create and Manage Candidate OutcomeModels, a moniker that conveys the overall functionality containedtherein. FIGS. 11-14 illustrate variations on combinations of theconstituent Models (and associated aggregations) may be selectively andnon-sequentially invoked by users and system processes to executeAssertional Simulations of different types.

Note that for graphical and pedagogic purposes, in FIGS. 11-14 and inother figures which depict the arrangement and action of theprogenitorial Models that contribute, in variations, to the consequentOutcome Models by means of execution of modalities of AssertionalSimulation, the depictions include the Models themselves (and otherfunctional blocks) but may not also show the plethora ancillary,supplemental and associated information aggregates that such operationsmay generate and which may accumulate over the course of an executionsequences, as well as such additional information as user and systemnotes, annotations, activity logs tags and other information that may be(or may have been) collected and/or harvested in any or all of thecreative and constructive and modification activities associated withthe creation of these informatic structures. Example depictions of suchModel-associated information may be found in FIG. 3 (see compositefunctional blocks 1308, 1309, 1310) and in FIG. 4 (see compositefunctional blocks 1405, 1406, 1406 and 1408) and in the associateddescriptions. Thus, in these and other relevant figures (anddescriptions) where such ancillary information may not be explicit, inthe absence of explicit statements and indications to the contrary, itmay be assumed that these attached aggregations are included within thedepicted operations and may be used as factors within any of theassociated methods and procedures and interfaces.

FIGS. 11-14 reveal a novel extension within the teachings embodiedwithin present invention, one that provides unique new capabilities tosuch varied technology-driven applications as modeling-simulationsystems, business, operational, financial and mathematical analysissystems, adaptive communication systems, financial transaction mediationsystems as well as the broad data management arena, to name but a few.In embodiments, a DTS Platform provides the systematic capability forusers and system processes to initiate permutations of combinatorial,branched, conditional and/or sequenced modes of execution of AssertionalSimulation upon optionally versioned Assertion-Apportionment pairs andReference Data Models. In these modalities and computationalconfigurations, users and system processes may invoke DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesto apply one or more elements within the projective execution chain ofAssertional Simulation between collections of one or more discrete and(usually but not exclusively) fully-formed Assertion-Apportionment pairsand collections of one or more discrete (and also typically but notexclusively) fully-constituted Reference Data Models to producecollections of Outcome Models, where each element within this resultscollection is associated with and reflective of an AssertionalSimulation of a particular progenitorial Assertion-Apportionment pairupon a particular Reference Data Model. The combinatorial and/orsequential execution of Assertional Simulation using combinations ofModels composed within these collections may be executed in a number ofdistinct possibly overlapping and, in variations, combined modalities,functional variations of a Platform 1000 that have broad application inapplication across a spectrum of markets.

Note that while FIGS. 11-14 reveal example variations of thesemodalities, others may be possible, and the exemplars depicted within(and others referenced by) these illustrations as well the accompanyingtext (and other relevant descriptions) are presented without foreclosingthe general applicability to and within other configurations andcapabilities and computational environments. Moreover, note, that,unless specifically noted, the operations depicted in FIGS. 11-14 (as inthe foregoing) assume general and unconditional applicability of any orall of the DTS-based (and/or DTS-enabled and/or DTS-accessible) methodsand procedures and interfaces described in this disclosure, including(but not limited to) those germane to and which may be applied to andwhich were cited in the context of the creation, assembly, manipulationof any variations of Reference Data Models, Assertion Models, andApportionment sub-Models or any associated ancillary structures. Inaddition, the example embodiments described here share the applicabilityand utility of other methods and procedures and interfaces related toany or all of the content-sensitive and content-responsive, content- andcontext-adaptive methods and procedures and interfaces described inexample implementations and example use-cases, as well as otherexecution and computational modalities associated with the mathematicalcomposition of Models upon one another (as well as any othermathematical operations in such contexts) as may be utilized in theexecution of variations of Assertional Simulation, including (but notlimited to) those associated with any or all of the looped recursiveoptimization operations previously described.

In embodiments, therefore, the application of varieties of combinatorial“churn” to any or all of the elements constituting the aforementionedcollections of constituent and progenitorial Models and the dynamicapplication of selected (and content-variable) modalities of AssertionSimulation may also be supplemented by the dynamic assembly (and invariations, in-context manipulation and modification andre-parametrization, as in the foregoing) of such combinations, though,as with other computational aspects within a Platform 1000, despite themany areas of commonality and the capabilities that permit conditionalapplication of shared methods and procedures and interfaces, thedivergent roles of Assertion-Apportionment pairs relative to ReferenceData Models give rise to differences in the specific computationalcomponents and the order in which they be may be applied. Whereapplicable, such differences may be noted but may be achievable by thoseskilled in these arts.

Referencing FIG. 11, the functional blocks and data stores (representingthe progenitorial elements in an Assertional Simulation and itsfundamental results) represent a basic and exemplary configuration ofAssertional Simulation, one referenced directly and indirectlythroughout the figures and descriptions within this disclosure and invarious concrete implementations of a DTS Platform 1000 and which, inembodiments, serves as both a common configuration for AssertionalSimulation but which also provides one foundation upon which otherwidely-varied and multiplicious modalities of Assertional Simulation maybe built. Further, variations of Assertional Simulation described inother modalities as depicted in FIGS. 12-14 (as well as in othercontexts, example and applications) may, in embodiments and someexecutional modalities contain one or more applications of the instanceillustrated in FIG. 11. This relation does not diminish the utility, thebreadth nor the novelty of those applications and configurations of theelements composing Assertional Simulation, since they are onlyoptionally and partially derivative from the example elucidated in FIG.11, with successor variations extending and enhancing that exampleinstance-case.

FIG. 11 illustrates a modality of Assertional Simulation wherein thereis one Assertion Model (Single Assertion Model 11005) projected upon asingle Reference Data Model (Single Reference Data Model 11004) butwhere there may be a collection of one or more Apportionment sub-Modelsassociated with and utilized in conjunction with the Single AssertionModel 11005, though, such associations occur as discrete one-to-onepairings in a given instance of an Assertional Simulation. As shown,users and/or system processes may select an Apportionment sub-Modelthough functional block Select Apportionment sub-Model(s) 11002, where,as with other descriptions and example implementations and modalities,the pervasive compound control/data flow Platform-wide Controls andInterfaces 1111 provides the required selection interfaces andparameterization information and capabilities as well as interface andfunctionality to previously-described platform-wide services such as,for example, APM and RAM mediation and the variety of methods andprocedures and interfaces that harvest, store and compute in-appactivity metrics and compile and maintain related logs. As depicted inFIG. 11, the collection of K Apportionment sub-Models from which a giveninstance of Apportionment may be chosen is illustrated as composed ofinstances Apportionment sub-Model 1 11000A through Apportionmentsub-Model K 11000B where this collection is designated by calloutApportionment sub-Models Collection 11001.

The output of functional block 11002, a data flow labeled SelectedApportionment sub-Model(s) 11003 provides the input to functional blockExecute Projective Assertional Simulation—Create and Manage CandidateOutcome Models 1116, the collection of DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces describedin foregoing as well as in subsequent figures and texts. In conjunctionwith the Models 11004 and 11005 (the previously cited single-instanceReference Data Model and Assertion Model, respectively), block 1116generates the associated Outcome Model associated with the applicationof Assertional Simulation to the particular instances of Models 11004and 11005 using the currently selected Apportionment sub-Model. Thisresult, labeled data flow Outcome Models 2103B is placed within ageneral data store DTS Outcome Models 11006. As discussed in othercontexts within this disclosure and in various concrete implementationsof a DTS Platform 1000 (see FIG. 2, for example, and related discussionsand subsequent examples and explanatory text related to compositefunctional block 1117 titled “Manage DTS Competitive and CollaborativeSimulation Environment with Optimization 1117”), in this non-limitingexample, Cardinal Outcome Models 1204 and Candidate Outcome Models 1205may be selected from a data store such as 11006, noting again that theDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces that execute such selections occur in manymodalities and in many computational contexts in a Platform 1000 andthat many alternate and functionally equivalent implementations may beused.

This mode of Assertional Simulation (referenced in DTS-terminology as“single Assertion-single-Reference” (noting the optional application ofvaried Apportionment sub-Models)) has many subtle variations and wideutility across a diverse range of applications, including, as noted, innon-DTS systems. This wide-ranging versatility is evident even in thismost elemental form but note that there are a number of variants of thismodality, extensions that are largely (but not exclusively) derived fromseveral premises. These premises or stipulations influence and, in somecases, determine a variety of structural and algorithmic arrangementsand combinations in associated execution chains. Note that thestipulations in the following paragraphs (and in other sections of thisdisclosure) are presented in the context of an explanation of thevarious modalities of Assertional Simulation and, in this connection (orothers), are not intended to be exhaustive; nor do they encompass allother operating principles that may apply (in contexts and by degree)within embodiments of a Platform 1000, noting that such operatingprinciples may not in all cases be explicitly described in the exampleimplementations within this disclosure but may nonetheless be evident invarious concrete implementations of a DTS Platform 1000 and may bepresent or achievable by those fluent in these arts.

Recall, however, that, as explained in previous descriptions of certainqualities of DTS models, in embodiments, the relationship between anAssertion Model and its associated Apportionment sub-Model implements avariant of logical association known as a hypostasis (or subjectalabstraction). The novel application and extension of this principle inDTS (called DTS Subjectal Coercion) is manifested in the fact that thenominal user- and/or system-supplied values associated with informaticnodes within an Apportionment sub-Model supply “ground truth” values (or“perceived-as-true” values) to corresponding elements within a“governing” model (where, in the present example, this “governing model”is an Assertion Model). The application of these principles (aptlydescribed and often utilized in Category Theory) in concrete form is thebasis of the DTS-based nomenclature that designates Apportionmentstructures as “sub-Models” in that sub-Models implement such a subjectalcoercion (as described) and are thus definitionally reflective of itsassociated “governing” Model, where, as stated, for convenience, inthese and other examples, the associated “governing” Model refers to theAssertion Model but which may, in variations, refer to other informaticstructures such as, for example, a Reference Data Model.

Note, however, that in the novel application of DTS Subjectal Coercion,in embodiments and in some contexts, the time base within a giveninstance of the Assertion-Apportionment pair governed by thedeterministic relational principles outlined here may exhibit any or allof the previously described eccentricities, asymmetries anddiscontinuities (and other time- and alternate-dimensional-relatednon-periodicities) where such irregularities may be asymmetric anddisjoint in relation to the explicit or inherent time- or-alternatedimension-based delimitations within one or more Reference Data Models.Further, note that such eccentricities (or all types) may result fromand/or may be derived from one or more properties within either thesub-Model or the governing Model or within both. Thus, in embodiments,any and all of the previously described time- andalternate-dimensional-base functionality may be applied in theseinstances and the variations cited and implied in this disclosure.

Thus, in view of both the foregoing considerations and the non-limitingpresentational convenience of designating the Assertion Model as thegoverning entity for the relation mediation coerced by DTS hypostaticabstracted upon Apportionments sub-Models (noting, as well, that, inpractice, the pairing of Apportionment sub-Models with AssertionalModels is more common), it may be seen that the structure of informaticnodes within an Apportionment sub-Model reflects the form and structuredefining the associated Assertion Model, either directly or, invariations, in equivalent forms which may, for example, result fromapplication of one or more provably bi-directional losslesstransformational operations, noting again that, in some variations, theDTS-specific application of DTS Subjectal Coercion may instead reflectthe form of one or more Reference Data Models or other informaticaggregates.

The first premise or stipulation is that each instance of this type ofAssertional Simulation (“single Assertion-single-Reference”) representsa discrete execution event where the unique end-point produced by thesecomputational operations is a particular instance of an Outcome Modelresulting from the application of the unique-to-that-instanceAssertion-Apportionment pair upon a given instance of a Reference DataModel. But note that the uniqueness of this Outcome Model is based notonly on the singular computational association of the particularAssertion-Apportionment pair with that particular Reference Data Modelbut that the Outcome Model may also be distinguished by the composition,sequence and parametrization (and other factors) of thecontent-sensitive and content-responsive methods and procedures andinterfaces selected and applied, as described in the foregoing. Thus,Outcome Models are bound not only to the precedentialAssertion-Apportionment and Reference Data Model instances from whichthey are produced but also to the computational and execution contextand algorithmic structures, conditions, sequences and internalparameters by means of which the simulation was executed.

This first premise leads to a second but largely derivative stipulationwherein both the parameters within the Apportionment sub-Model and theparticular set of content-sensitive and content-responsive methods andprocedures and interfaces in any given Assertional Simulation jointlyprovide the point of distinction differentiating each such executioninstance from other such instances but that the singularity of thisApportionment sub-Model instance applies only with respect to thoseparticular Assertion and Reference Data Models and the means by whichthe Outcome Model is produced. Thus, an Apportionment sub-Model need notbe identically unique across all instances of Assertional Simulations.These stipulations are illustrated by the fact that the Outcome Modelproduced by a particular parametrization within an Apportionmentsub-Model and applied to a given Assertional Model-Reference Data Modelpair using a particular suite and sequence of methods (andparameterization uniquely spawned or harvested for this instance) isunique and distinct from an Outcome Model produced by application of anidentically configured (and even duplicate) Apportionment sub-Modelusing either a different Assertion Model or a different Reference DataModel (or both) and/or a different assembly and/or parameterization ofexecution mechanisms, even in the event that the respective Outcomes maybe nominally identical in all respects.

Among the consequences of these stipulations is a further provision thatthe instance-unique constituent Models and the instance-uniquecollection and sequence of parameterized execution mechanisms assembledfor a given execution instance combine to produce a member within acollection of Outcome Models that binds these progenitorial or, inDTS-terminology, “ancestral” Models and associated procedures,parameters and execution sequences in an identity-unique association. Inconjunction with the antecedent stipulations, this associationalimperative provides one set of pervasive and foundational operatingprinciples and contexts within which and by means of which DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces systematically instantiate the various applications andmodalities of Assertional Simulation (and associated and ancillaryoperations).

These foundational operating principles apply, in embodiments, acrossthe entire range of functionality and services provided by and within aPlatform 1000. One tangible example result may be seen withinembodiments of the previously described Associational Process Management(APM) skein wherein these stipulations may confer a variety oforganizational and algorithmic mandates as well as parameters foroperating variables upon the DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces that provide andmaintain indexing and identity management for the elements that composesuch instances. Such mandates and variables may, in embodiments, also beconditionally applied by degree based upon one or more system and/oruser-mandated conditions as well as upon elements that may be derivedfrom or required by or within the current application or executioncontext. These conditionally applied services include maintenance of oneor more aspects that define the binding of constituent Models andprocedures as described above but which also maintain and apply suchsystem-mandated and platform-wide capabilities as RAMS-mediated accessand execution capabilities and the skein of entity ownership that mayapply to any of the elements within such Models as well as to theapplied methods and parameters. The execution-driven (or event-derived)associations mandated in the third stipulation, for example, may beintegrated within and mediated by not only the APM-mediated ownershipindexing capabilities (as described previously) and the previously-citedRAMS capabilities but may also impel RAMS and other capabilities tospawn entity association certificates (or the equivalent), as describedin this disclosure including (but not limited to) user-to-process,process-to-process, process-to-data and data-to-data associationcertificates which may be integrated within the DTScertificate-interconnection and certificate-enchainment schema, asdescribed in subsequent paragraphs.

In a further example, while the associative details described in thepreceding paragraphs may, in variations, be preserved in one or moreassociational certificates spawned and maintained within the APM andRAMS system access mediation system, all of the foregoing may beexecuted in the context of and in association with the previouslydescribed looped, iterative and recursive operations and made availablethrough composite functional box 1105 (see FIG. 1) and distributedwithin the system-wide control/data flow 1108B (titled “OptimizationControl”, also in FIG. 1) illustrated in the figures and descriptionswithin this disclosure and in various concrete implementations of a DTSPlatform 1000 as subsumed within control/data flow 1111 (“Platform-wideControls and Interfaces 1111”). Note that in such contexts, any or allinformation created or derived as a result of the platform-wide mandatesand variables described in the foregoing may be preserved and indexed, aDTS feature that, as mentioned in prior discussions, comprisesfunctional increases in the Shannon entropic richness, depth and densityof probabilities associated with representations of this information.Such enrichment may be utilized by DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces which may apply avariety of mathematical and algorithmic and analytic and transformationtechniques, including (but not limited to) statistical analysis, patterndetection, feature extraction, content- and context-derived parametersthat may be applied to machine learning and adaptive mechanisms. Notefinally, that any or all of the information derived from or related toany of the foregoing, including the association of assembled processesand Models (and the users and other entities involved in such executioninstances) may be included within the previously described platform-widein-App metric capabilities where such additional information may then beused in any or all of the responsive capabilities provided within aPlatform 1000, providing a suite of self-reinforcing content- andactivity- and context-derived feedback mechanisms, as described in otherparts of this disclosure.

In the current context of FIG. 11 and the explanation of the “singleAssertion-single-Reference” modality of Assertional Simulation, let acollection of instances of Single Assertion Model 11005 be calledAS_(A-thru-M) and let a collection of instances of Reference Data Model11004 be called RDM_(A-thru-Q) where “A” represents 1) an index of aninstance of an Assertion Model within M possible instances; and 2) anindex of an instance of within “Q” possible instances of Reference DataModel. Let a user (and/or system process) create Assertion Model AS_(A)and assembles RDM_(A) (using any or all of the previously describedDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces). Then, using the capabilities provided byblock 11002, let the user select an instance from Apportionmentsub-Models Collection 11001, in this case Apportionment sub-Model 111000A (called here APP₁). Finally, let DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces select asequenced composition and parametrization of content-sensitive andcontent-responsive methods and procedures and interfaces (as describedin the foregoing) called E_(A-A-1-X) where the subscript A-A-1-Xdesignates the execution of Assertional Simulation using configuration Xof composition and parametrization arrangement E (where X represents aninstance drawn from the combinatorial total of Z possible configurationarrangements). Thus, upon invocation of block 1116, this combinationproduces Outcome Model_(A-A-1-X). Referencing FIG. 11, note again,however, that, in this case, there is only one Outcome Model, so theCardinal and Candidate Outcome Models 1204 and 1205 are identical,though, in embodiments, as noted, methods and procedures and interfaceswithin the APM indexing schema may instantiate two copies of thisassociational collection (where one would be designated Cardinal and oneCandidate) or may annotate the elements as comprising executioncollection E_(A-A-1-X) as well as the progenitorial Models as associatedwith both a Cardinal and Candidate Outcome Model.

Finally, note that under user- and/or program-based control (asdescribed in the connection with FIG. 6 or other variation of iterativeand recursive operations), this procedure (and any of its many variants)may be repeated with successive application of instances from thecollection 11001 (Apportionment sub-Models Collection 11001) against thesame instances of Assertion Model AS_(A) and Reference Data ModelRDM_(A), where each Apportionment sub-Model may be modified andre-parameterized (using any or all methods as described), therebyproducing a collection of Outcome Models, indexed as OutcomeModel_(A-A-1-X) through Outcome Model_(A-A-K-X). Thus, in the firstcombinatorial case, where only the Apportionment sub-Models aresuccessively applied in the execution sequence, users and/or systemprocesses may select a Cardinal Outcome. But note that, again, underalgorithmic or user control (or a combination), the executionconfigurations X-Z may be applied successively against each suchinstance, creating a superset of the collection of previously referencedApportionment sub-Model-only Outcome Models (and the associatedprogenitors, as described), which in this example may be indexed as acollection of Outcome Model_(A-A-1-X) through Outcome Model_(A-A-K-Z).Further, in variations, the Reference Data Models may also be adjusted,and the combinatorial sequence re-executed, yielding another supersetencompassing Outcome Model_(A-A-1-X) through Outcome Model_(A-Q-K-Z),where, as before, all of the progenitorial Models and each executionconfiguration and parameterization may be preserved. Finally, theAssertion Models may also be changed, but this may result in a largeincrease in the combinatorial complexity due to changes that may berequired in each associated Apportionment sub-Model as a result ofstructural changes in the Assertion Model, a consequence of thepreviously referenced relation-mediating DTS Subjectal Coercion coercedbetween these informatic structures. Thus, for each change in AssertionModel, there may be a full set of execution cycles for each variationfor each parameter in the associated Apportionment sub-Model. Thus, inthe limit, there may be M variations of the Assertion Model which wouldmean that there would be M instances of each of the K Apportionmentsub-Models which must be applied in Z execution parameterizedconfigurations against Q instances of Reference Data Model, where again,in embodiments all the antecedent Models and execution configurations(as well as any ancillary, supportive and other related information) maybe preserved and indexed as described previously and may be subject toand/or included within any or all of the optimization, APM, RAMS andin-App activity measurement facilities available within a Platform 1000.

Note that, in the limit, this example can, in some situations, yield alarge number of Outcome Models (and ancillary information, as in theforegoing). Using combinatorial math, in this example case, if there are5 instances of each variable, these operations would yield 27,405separate instances of Outcome Models. In the context of certain types ofAssertional Simulation, such combinatorial results are not unusual andefficiencies in computation and storage may be implemented and optimizedbut illustrate one manner in which one or more elements within thepreviously described platform-wide capabilities may be applied.Additional and increasingly complex variations may be easily seenwherein, in a non-limiting example among many possibilities, an OutcomeModel may be dynamically substituted as a Reference Data Model in thecontext of any such operation and an entirely new branch of the layeredlooping sequence may be created. This one example of how the layeredcombinatorial looping in this modality may substantially increase thearray of Outcome Models and the associational bindings, and, inembodiments, DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces may be supplied which specificallyaddress not only ways to accommodate and manipulate such growing amountsof computations and information but which may be used to aid inrealization of one or more of the goals of the execution, includingthose outlined in subsequent paragraphs (and elsewhere in thisdisclosure) such as for example, selection of Cardinal and CandidateOutcome Models 1204 and 1205 (in FIG. 11 and other figures).

Note as well, however, such combinatorial enrichment of information mayresult from another common mode of execution of Assertional Simulationwherein, given any or all of the above combinations, separatelyparametrized Apportionment sub-Models may be applied with a givenAssertion Model not only upon the entire Reference Data Model (asdescribed in the preceding paragraphs and in examples throughout thisdisclosure) but also (or instead) upon one or more nodes within a givenReference Data Model. This variation is discussed in more detail inreference to FIG. 12 in subsequent paragraphs, but in the presentconnection, such additional and/or supplemental computations may becontextually invoked either in a standalone sequence or within thecontext of any variation of the combinatorial operations in the presentexample (as well as within looped recursive optimizations, asreferenced). The criteria for selecting methods as well as the types andvariety of parameterizations within the selected Apportionmentsub-Models as well as which Assertion Model is selected (which in turnwill change the type of Assertional sub-Model and the relevantparameterizations) may be determined or influenced or otherwisemodulated, directly or indirectly, by such factors including (but notlimited to): user- and system-provided input; compositional andstructural context and parameters as may be derived or inferred by meansof content-sensitive and content-responsive methods and procedures andinterfaces (as in the foregoing); and any of the other products orresults that may be derived from or inferred from any of computationalinstruments described in examples and descriptions throughout thisdisclosure. Note as well that application of this variation may, in thepresent combinatorial example, increase the number of possible OutcomeModels but also may increase the diversity and richness within thiscollection, further contributing to the overall increase to the Shannonentopic richness, as described throughout this disclosure as a pervasivefeature in implementations of DTS Platforms. Finally, as discussed, anyand all such information (and derivatives of such information) may begathered and included within other related and non-related calculationsincluding those connected to the computation on in-App activity metricsand the system-wide adaptivity, as described in the foregoing.

Note, however, that these predicates reference, highlight and exemplifyanother novel aspect of the DTS Platform: the multi-factor,multi-layered and dynamic and evolving adaptivity that pervades itsconstituent methods and procedures and interfaces. This may be seen inmany examples through this disclosure and in concrete implementations ofone or more components of a DTS Platform 1000, but in the presentcontext, note that since each execution sequence that results in anOutcome Model is produced as a result of a unique-to-that-instanceassociation of certain data and parameterized and sequenced processes(association that in some in some cases, include one or more users) andsince this association is tied to an end-point (the Outcome Model) whichhas or may be given or assigned a normative disposition by users and/orsystem processes (where one such example is the designation of oneOutcome Model as Cardinal over other Candidates or the ordering of acollection of Outcome Models by any number of user- and/orsystem-supplied criteria this yielding a normative ranking of theOutcomes) and since the details composing thisassociation-skein-to-end-point are now indexed as such a set (and which,in embodiments, may be also be memorialized or preserved within one ormore RAMS-administered associational and/or event certificates or theequivalent), DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces within the in-App activitymeasurement and metric capabilities, as described, may integrate all orpart of this information or derivatives thereof, providing variables andparameters that may be used by users and processes throughout a Platform1000, including, for example, by pattern detection and recognitiontechniques and facilities. Such capabilities and results may, inembodiments, be utilized in many aspects of the system but in thepresent exposition of the DTS-unique adaptive capability may be employedto dynamically modify, reparametrize, adjust, prioritize or otherwisemodulate any of all of the operations within any DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures andinterfaces. In embodiments, therefore, such widely-based closed-loopadaptivity (and other variations in which such activity and resultsharvesting may be utilized) may also evolve over time and/or based uponusage and, in variations, may be based upon or may integrate factorsderived from other instantiations of DTS Platforms 1000. Moreover, suchevolving functionality may integrate many types of ancillary informationand processing capabilities, such as, for example, direct and topicaluser input and inferred information that may be derived from manyvariations of statistical analysis, AI and machine learning systemexpert-systems.

The variations of multi-layered concatenation and combinatorialsubstitution and iterative and recursive looping that may be applied inboth simple and complex configurations within the“single-Assertion-single-Reference” modality of Assertional Simulationis a common operational mode within activities in a Platform 1000. Whileat the limits, in embodiments and contexts, especially as may bedeployed in machine learning and recursive derivation of Outcome Models(variations of which are explored in connection with AssertionalSimulations illustrated in FIG. 14 and in other examples anddescriptions of DTS throughout this disclosure), applications andassociational-contexts, users and system processes may also be engagedin simpler forms where, for example, users (or system processes) mayseek to select Cardinal and Candidate Outcome Models 1204 and 1205 (inFIG. 11 and other figures). Note, however, that, in embodiments and somecontexts, there are numerous modalities within which the 1204 and 1205may be selected, and in some instances, the means and mechanisms thatanimate such selection are related to not only the context within whichthe instances of Assertional Simulation(s) are executed but also to bothuser- and system-driven intentionality, but also based upon or derivedfrom factors embodied in the nature, structure and composition of therespective Models.

In one non-limiting example, in embodiments, the structure andcomposition of Models and the execution modalities may be explicitlyconfigured to reflect a context-derived and/or a user- or system-drivenintentionality (harvested or derived implicitly or explicitly) thatrequires selection of a single Outcome Model as Cardinal while any otherexample instances that have been spawned within such a goal-orientedcycle are designated as Candidate Models. As discussed, this comprisesthe zero-sum game case (one winner) and, in embodiments, user interfacesand system modalities may be conformed to assist in the configurationand execution and optimization of this type of result, or, stateddifferently, for modalities (sometimes optionally) configured to producea zero-sum outcome.

In other modalities, Assertional Simulation may be configured in othergame-theoretic modalities, and, as in the zero-sum case, relevant userand system interfaces may optionally (and even dynamically) accommodateand facilitate such activity. Thus, in embodiments and in certainoperational contexts, any or all of the foregoing methods to evaluateOutcome Models (as well as others that may contribute to such evaluativeprocesses) may be contextually or statically configured to yieldcomparative results rather than zero-sum winners of losers, theso-called non-zero-sum case wherein instances of Outcome Models withincollections of such Models may be ranked or evaluated relative to othersuch instances (within and/or outside the collection) based directly orindirectly upon any or all of the following non-limiting cases in anycombination: i) relative to one-another based upon one or more factorswithin and/or external to the Outcome Model instances, yielding one ormore ordered lists; ii) relative to one-another in relation one or moreuser- or system-supplied-criteria or derivations of such criteria; iii)relative to one or more aspects internal to or derived from or otherwiserelated to one or more aspects within other instances as in i) and ii)above; iv) relative to one-another as i)-ii) above but additionally onthe basis of information which may be inferred from or which may bebased within one or more synthetic or idealized instances of OutcomeModels where, in embodiments, such idealized instances may be selectedand/or created and/or modulated and/or otherwise instantiated by usersand/or system processes from prior Outcome Models (using one or moreaspects in i)-iii) in the foregoing) and/or by other means and basedupon information extracted from, derived from or otherwise containeddirectly or indirectly from or within any of the foregoing including butnot limited to: a) from any or all of the elements within or associatedwith the relevant progenitorial Models; b) and/or from any collection ofassociationally-bound parameterized, sequenced methods; c) and/or fromone or more elements within or which may be derived or inferred fromsuch parameterized, sequenced methods; d) and/or from any combination ofa)-c) above of inter-instance progenitorial Models and/or parameterized,sequenced methods. But note, as well, that in embodiments, suchoperations may also optionally and dynamically include all or part ofany of the foregoing from one or more sources outside the subjectinstance of a DTS Platform 1000, including from other separate (butpossibly related) instances of DTS Platform 1000.

Thus, in embodiments, the evaluation criteria in these non-limitingexamples may be used in the context of a wide variety of DTS-managednon-zero-sum game states and conditions and may be employed in a varietyof applications of Assertional Simulation, including cases involvingevaluation of business initiatives, the apportionment of resourceswithin levels of a taxonomic tree and in transaction facilitationenvironments. The game theoretic aspect of functionality, elements andcomponents throughout a Platform 1000 as instantiated in DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces is discussed in numerous contexts and example implementationswithin this disclosure, in general but not always in connection withcomposite functional block 1117 (“Manage DTS Competitive andCollaborative Simulation Environment with Optimization 1117”) in FIG. 1and other figures.

FIG. 12 illustrates an example configuration of the modality ofAssertional Simulation that encompasses certain applications andvariations of the “single-Assertion-single-Reference-Model” modality.These cases fall within a collection of execution chains within aPlatform 1000 called the “single-Assertion-multiple-Reference-Model”modality of Assertional Simulation. In certain respects, it may appearthat this modality is a subset set of the previously describedcombinatorial variation within the “single-Assertion-single-ReferenceModel” modality, and in some ways, at least in principle and in someexecution details, this may be true since, in some variations of theprevious case, a single Assertion Model may be composed upon successiveinstances of Reference Data Models using any or all instances ofvariously parameterized Apportionment sub-Models. But the“single-Assertion-multiple-Reference-Model” modality in FIG. 12 depictsthis specific instance in more detail in a non-limiting example form,and is indicative of the fact that, in embodiments, not only is this isa common mode within and available to numerous applications in variousexecution modalities and contexts of Assertional Simulation but that, asin other cases, specific user interfaces and user- and system-drivencapabilities may be optionally and dynamically or statically provided tofacilitate and enhance this modality.

Moreover, the “single-Assertion-multiple-Reference-Model” modalitydiffers from the earlier instances in that in many such cases, thecollection of Reference Data Models may not be permutationally spawnedas may be done in many (but not all) instances in the context of thepreviously described layered looping execution chain. Rather, in thismodality, the subject Reference Data Models may already be pre-formedand designated previously as either Candidate or Cardinal Reference DataModels, noting that any or all elements within these Reference DataModels may have been developed or generated or manipulated or otherwisetransformed previously (or, in variations, in the context of one or moreexecutions cycles in the present example) by means of any or all of themethods and procedures and interfaces described in the foregoing. In anycase, one aspect (but as may be seen, not the only aspect) thatdistinguishes the example configuration of Assertional Simulationdepicted in FIG. 11 from the current modality is this delimitation incombinatorial possibilities.

Finally, in this modality, a common execution mode, referenced in theprevious example, allows users and/or system processes to create one ormore Outcome Models as a result of the application of a series ofseparately parametrized Apportionment sub-Models (with a SingleAssertion Model) upon one or more discrete elements within the subjectReference Data Model rather than upon its entirety. This variationproduces a type of variegated Reference Data Model wherein a variety ofparameterizations within various Apportionment sub-Models are coercedthroughout the Model, a variation of mathematical composition and, insome contexts, of the DTS informatic convolution described in theforegoing. Though this variation of Assertional Simulation was brieflyreferenced in descriptions of the previous modality, in the presentconfiguration (“single-Assertion-multiple-Reference-Models”), thismodality, in certain embodiments and application in such embodiments,may be more common and may, indeed, constitute a prominent feature ofthis type of Assertional Simulation, a possibility that may be evidentin example applications of the capabilities and functionality providedby and within a Platform 1000 illustrated throughout this disclosure.

Note as well, however, that, in embodiments, any or all of thepreviously described capabilities and interconnected platformcapabilities that may be applied in the“single-Assertion-single-Reference-Model” apply as well in this“single-Assertion-multiple-Reference Model” modality, including but notlimited to: APM indexing, RAMS capability mediation, systematicharvesting of in-App activities, adaptivity and evolution of methods andprocedures based (at least in part) upon configurations and results, aswell as the various modes of game-theoretic competition. In cases whereany such capabilities may not apply, or if other non-cited andadditional capabilities may be applicable, such instances may be noted.

Referencing FIG. 12, a collection of N Reference Data Models isstipulated and referenced by group callout Two or More Reference DataModels 12001 composed of data stores containing Reference Data Model 112000 through Reference Data Model N 12002. Using methods and proceduresand interfaces within or available to composite functional block SelectReference Data Models 12003, a Reference Data Model from collection12001 may be selected using any or all of the means described in theforegoing example in FIG. 11 relative to block Select Apportionmentsub-Model(s) 11002 (including, for example, content-sensitive andcontent-responsive methods and procedures and interfaces and otherplatform-supplied capabilities, as described). Note that block 11002 andthe collection of K Apportionment Models within Apportionment sub-ModelsCollection 11001 is also depicted and operates as described in theprevious examples in FIG. 11. Finally, the data flow outputs of blocks12003 and 11002 are indicated by callouts Data Reference Models 1108 andSelected Apportionment sub-Model(s) 11003 and presented to block 1116(entitled “block Execute Projective Assertional Simulation—Create andManage Candidate Outcome Models”), the composite functional blockpresent in numerous figures and described in associated descriptions andexamples within a variety of contexts and variations. In the currentexample illustration, the output of block 1116 is the bifurcated dataflow referenced by callout Outcome Models 1203 (shown in FIG. 2 andother figures) where one component of this data flow contains a CardinalOutcome Model and the other contains a Candidate Outcome Model. TheseOutcome Models are shown as composed within a sorted and indexedcollection of Outcome Models referenced by callout 12008 (“Collection ofMultiple Discrete Outcome Models—Single Assertion Model projected acrossN Discrete Reference Data Models using K Apportionment sub-Models12008”) where the M Candidate Outcome Models are stored within datastores Candidate Outcome Model 1 12004 through Candidate Outcome Model M12005 and the Q Cardinal Outcome Models are stored within data storesCardinal Outcome Model 1 12006 through Cardinal Outcome Model Q 12007.

As mentioned, while the configuration in FIG. 12 may be seen as avariation within the previously-presented examples of“single-Assertion-single-Reference-Model” Assertional Simulation, asnoted, in embodiments and in applications of this modality, the present“single-Assertion-multiple-Reference-Model” modality may be implementedas a standalone execution modality or may constitute a primary executionnexus for many users. The central role of this configuration ofAssertional Simulation in certain applications may be seen in anon-limiting but exemplary example of its use in profit centeroptimization, an expansion of previously-cited examples. Recalling anearlier example of this type, a manager responsible for Profit Center Ais engaged in a budgeting exercise using the tools of AssertionalSimulation as described in that example. But note that, in this case,the manager derives and manipulates a set of Apportionment sub-Modelparametrizations using (in this instance) a single Assertion Model (alist of Profit Centers in this case which may, in variations be ataxonomic hierarchy) and applies this collection of Apportionmentsub-Model parametrizations to a collection of selected Reference DataModels. The various Apportionment sub-Model parametrizations associatedwith this Assertion Model are, in this case, the user and/orsystem-nominated allocation of indirect expenses to actual profitcenters within the Company where the form and structure of these profitcenters are contained within the Assertion Model (described in the aboveas one-dimensional list or as a hierarchy) and initialized either by oneor more users and/or through system-facilitated extraction from theReference Data Model or by other means, a structure that is coerced uponthe Apportionment sub-Models through the previously described DTSSubjectal Coercion. Note that in such scenarios, DTS semanticde-referencing is commonly invoked (implicitly or explicitly) todisconnect the information within the Reference Data Model from itsaccounting system ontology, requiring, in those instances that the termswithin the Assertion Model that also appear within the Reference Datamodel be reconciled and correlated.

Further, the multi-layered nature of the game-theoretic basis of many ofthe executional modalities and user and system interfaces, previouslyreferenced and described, may also be seen here. In the context of theapplication of Assertional Simulation to accounting information reposedwithin a Reference Data Model, note that the taxonomic structure of theChart of Accounts (the typical Reference Data Model used in thesescenarios) requires that the value of a parent node not exceed thearithmetic sum of its children. This stipulation upon a nested structureis described by the DTS-nominated term “DTS rule of descendantsummation”, a term derived in the course of the development ofimplementations of various modalities of Assertional Simulation inPlatforms 1000 for applications typically but not exclusively related toaccounting-based environments. In these environments, the Reference DataModel is natively structured within the accounting domain to comportwith the requirements of the lexical and grammar derived from theconstraints of that ontology. As such, it is known as an instance of anested subsumptive hierarchy (or a nested partially ordered set ornested poset) but with the additional native-ontologically-spawnedstipulation of the DTS rule of descendant summation, a grammar-basedrule that is preserved in this instance during the application of DTSsemantic de-referencing. Apart from the particular algorithmic andcomputational requirements and execution implications of compilinginformation within such a structure with the mandates of DTS rule ofdescendant summation, in this connection, note that since each set ofchildren has one and only one parent (this is a consequence of thenested subsumptive property of the structure), a given parent mustdivide the sum of the values of its children among the Profit Centerswithin the Assertion Model. This is a mathematical consequence of thenode-by-node coercion of the Assertion Model upon this Reference DataModel hierarchy as executed in this modality of Assertional Simulationand the rules imposed on the structure. Thus, since the value attachedto a parent node must be the sum of the children (due to the DTS rule ofdescendant summation) and there is only one such parent (due to thenested subsumptive property of the hierarchy), in the coercion of theAssertion Model upon this collection, the value of each child must beallocated on a percentage basis to each profit center, a percentage thatis provided by the Apportionment sub-Model, the structure of which isidentical to the Assertion Model due to the application of DTS subjectalCoercion. As mentioned, if the Assertion Model is structured as aone-dimensional list, the coercion of this structureAssertion-Apportionment pair upon the collection of children of a givenparent yields a 3-dimensional informatic entity where the thirddimension (the parameterized Assertion-Apportionment pair) is linearlyindependent of the structure upon which it is projected. Thus, inembodiments, the Apportionment sub-Model may reflect this requirement bysupplying a percentage mapped to each profit center in the AssertionModel to each node, where the total, of course, cannot exceed 100%.

Note, however, that the allocation of percentages of costs to profitcenters is actually an example of a variation of a non-zero-sum gamewherein the profit centers within the Assertion Model are all relativewinners due to the division of the total among its constituents, andthere is no overall “victor” as there must be in a zero-sum game whereinthere is a single winner and the remaining non-victorious sibling nodeswould be relegated as “losers”. Of course, a manager in this example mayview the assignment of a smaller relative percentage of a given totalcost (represented by Chart of Accounts entry) as a victory, this ispurely subjective for many reasons and the fact remains thatdefinitionally, such percentage-based divisions (in the absence of othergame rules) is a non-zero-sum game. Note also that this fact remainstrue even if a node is allocated 100% of the available resource (therebyforcing its siblings to 0).

Thus, this common use-case provides another example of the novelmulti-modal, game-theoretic nature of imbued within modalities ofAssertional Simulation (qualities present, as well, as in otherfunctional components within a DTS Platform 1000): an Outcome Model inthis situation is composed of a set of discrete non-zero-sum gamesinstantiated among each collection of child nodes of each parent whereineach element within the governing Assertion Model (the structuredenumeration of a collection of profit centers) “competes” forApportionment sub-Model-supplied percentages as applied to each child ofthe current parent but such that these percentages divide the availabletotal among the sibling children. This process is repeated upon everyset of child nodes, from the terminal nodes of each branch of the treethrough its apex.

As mentioned, however, in many environments, the elements within theAssertion Model—in this instance, profit centers within anorganization—are not abstractions, though in many cases, even in thismodality, some such elements in the Assertion Model may indeed besynthetic, constructed for the purpose, for example, of running“what-if” scenarios, a strategy illustrated and discussed throughoutthis disclosure and as implemented in concrete form in operatinginstances of Platforms 1000. Rather, the profit centers in this examplerepresent real entities with actual personnel responsible for theirperformance and profitability. In the present example, another manager,called manager B may be responsible for a different set of profitcenters than manager A and thus, in the course of budgetary negotiation,would have his own view of the appropriate percentages within theApportionment sub-Model, one that, in general, would differ from managerA. This means each manager would create their own Outcome Model whereinthe Assertional Simulation coerces a commonly-shared Assertion Modelupon the commonly-shared Reference Data Model usingunique-to-that-manager (or to the instance a manager runs in pursuit ofthat manager's view of the optimal formulation) parametrizations of theassociated Apportionment sub-Model. Thus, each manager has applied theirown collection of discrete non-zero-sum construct that reflects theirview of the “ground truth”, or their view of what the “ground truthshould be.

The collection of discrete non-zero-sum competitions embodied withinOutcome Models executed within the application of theAssertion-Apportionment pairs reflects an actual situation common inmany organizations where managers responsible for such units compete forfinite resources—in this instance, for greater percentages of theallocations applied to their units within a given Chart of Accounts item(the resource) as contained within the Apportionment sub-Model. Thus,each manager would formulate their own Outcome Model which, asmentioned, in this instance and in this modality of AssertionalSimulation, is composed of a structured and enumerated collection ofnon-zero-sum outcomes. But in the end, in general, whether by fiat or byconsensus, a single set of Apportionment sub-Models prevails, thusnominating and designating the Cardinal Outcome Model, a zero-sum resultconferred upon a collection of non-zero-sum results wherein othercompeting instantiations are now called Candidate Reference Data Models,as shown in FIG. 12 and illustrated by callout 12008 referencing the MCandidate Outcome Models and the Q Cardinal Outcome Models.

In this sense, the non-limiting example in FIG. 12 reflects andexemplifies a novel instantiation of the DTS-engendered layeredapplication of game theoretic constructs, expressed in this instancewithin a modality of Assertional Simulation and which, in embodiments,are composed within operational and user-facing components of a Platform1000, a unique contribution within the teachings advocated in thisdisclosure.

This accounting-based example (and the many variations that may beinferred by those skilled in these matters) illustrates one use ofAssertional Simulation (and the variety of services and functionalityprovided by and within a Platform 1000) to produce a set of variegatedOutcome Models by application of a view of “ground truth” structured andparametrized within a series of Assertion-Apportionment pairs, aformalized set of judgements coerced upon a elements within a successionof Reference Data Models. As may be seen, however, any or all of thevariations and DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces described in the previousmodalities of Assertional Simulation may be applied here, revealing therich detail that may result from such invocations. As discussed,application of these previously combinatorial and, in embodiments,application of looped and optimization-directed methods and proceduresand interfaces serve many purposes within a DTS Platform 1000 and as maybe supplied to internal and external non-DTS computational andprocessing facilities, as described in the previous paragraphs andthroughout this disclosure.

Note that a DTS-platform may facilitate access to accounting and othertime-based data that provides views thereof for time periods (e.g., oddcount of weeks) that business operational and intelligence accessmechanisms may not provide. One aspect of a DTS-platform that mayfacilitate such access is the use of de-referencing that, as describedmore fully herein, releases content of a data system, such as anaccounting system, from constraints imposed by the system, such asmonthly accounting and the like. By applying capabilities of a DTSplatform, such as Assertional Simulation and the like, a user mayredefine accounting data from being accessed in monthly time-units toany other time unit, such as weekly, and the like. Through configurationof an Assertion Model, a user may take de-referenced accounting data andproduce a view of the data for a given six-week period, such as, forexample, defining a six-week window of time that captures accountingdata from a Reference Data Model that was configured through, amongother things, de-referencing source accounting data from at least oneaccounting data source. The application of multiple Assertion Modelsthat each provides a specific view of the accounting data(de-referenced) using Assertional Simulation may facilitate identifyingan impact on portions of a business that could not be done without suchcapabilities. In an example, a resource, such as a conference room of acorporation may be deemed in the source accounting system to be an assetthat is equally shared among all departments. Therefore, each month eachdepartment is charged for an equal share of the charges for the room.Through use of a DTS platform, Assertion Model(s) may be configured thattake information from, for example, a conference room reservation systemto determine, through application of the Model to the de-referencedaccounting system data using Assertional Simulation a time-usage-basedallocation of charges for the conference room. A result of suchAssertional Simulation may be an outcome model that represents impactson each department's cost accounting that reflects their scheduled userather than a general flat distribution of costs. Further, this timeusage-based accounting may be applied outside of the departmentstructure to capture, for example, charges of use of the conference roomthat should be applied to a specific project or other business activity.As an example, a six-week contract to perform a service may be awardedto a company. The company may choose to co-locate the team for thisservice in a shared facility, such as a conference room for the contractduration. Costs of the conference room the team, and the like for thissix-week period may be determined, through use of the methods andsystems of Assertional Simulation as described herein by casting anAssertion Model that facilitates identifying the resources consumed bythis project over the six-week contract period. Further in this example,employee expenses, such as indirect expenses, for this six-week periodmay be distinguished and allocated to either the project or to theemployee's base department on terms applied in the Assertion Model. Ifan employee assigned to the project takes a vacation during thisproject, salary and other expenses of the employee during their vacationmay be optionally allocated to the cost of accounting of the employee'sbase department rather than to the project.

The principles outlined in the preceding examples are furtherillustrated in FIG. 13 which depicts another variation of a modalitywithin Assertional Simulation called“multiple-Assertion-single-Reference-Model”. As in the previous example,this modality may, in principle, be executed as a variation within the“single-Assertion-single-Reference-Model” modality but, in a similarfashion to the “single-Assertion-multiple-Reference-Model” case, the“multiple-Assertion-single-Reference-Model” modality exemplifies certaincommon applications within a Platform 1000 and in tangible instances ofits deployment. As in the previous example, therefore, in embodiments,certain user interface and system capabilities may be optionally and/orconditionally optimized and/or oriented to accommodate and amplifycertain functionality and operational modalities provided within andappropriate for this type of Assertional Simulation.

FIG. 13 shows N instances of Assertion Models represented in data storesAssertion Model 1 13000 through Assertion Model N 13002 designated bycallout Two or More Assertion Models 13001. As in the previous examplesin FIGS. 11 and 12, the collection of K Apportionment Models withinApportionment sub-Models Collection 11001 is also depicted in FIG. 13and operates as characterized in the accompanying descriptions. In thecurrent illustration, in a manner that exemplifies and uses the fullbreadth of capabilities similar to those described in FIGS. 11 and 12and in other figures, examples and descriptions in other contexts, themethods and procedures and interfaces within or available to compositefunctional block Select Assertion Models 13003, permit users and/orsystem processes to select an Assertion Model from collection 13001.Likewise, block 11002 with similar functionality as depicted anddescribed in the previous examples in FIGS. 11 and 12 permits selectionof an Apportionment sub-Model from the collection of K Apportionmentsub-Models 11001. Thus, an Assertion-Apportionment pair is routed as aninput data flow to block 1116 (entitled “block Execute ProjectiveAssertional Simulation—Create and Manage Candidate Outcome Models”)along with Single Reference Data Model 11004 depicted in and describedin connection with FIG. 1, FIG. 11, 12 and other figures.

Note, however, that in a fashion consistent with the operation of DTSSubjectal Coercion, the selection and parametrization of anApportionment sub-Model is executed by users or system processes suchthat the selected sub-Model either comports with the current AssertionModel (as in the foregoing description DTS Subjectal Coercion) or maycoerce its structure upon a newly-spawned Assertion Model which would beinstantiated in the event that no current Apportionment sub-Model iscompliant with any of the existing Assertion Models. This operationalcharacteristic is implicit within and required by the bi-directionalnature of DTS Subjectal Coercion (as revealed in the underlying CategoryTheoretic mathematics from which it is derived and permuted) and isillustrated in the previous and subsequent examples describing theassociation defining an Assertion-Apportionment pair. Note that methodsand procedures and interfaces attendant to such operations may, invariations, employ one or more components of the DTS-based (and/orDTS-enabled and/or DTS-accessible) content-sensitive andcontent-responsive methods and that such “reverse synthesis” may beexecuted in many contexts. Thus, while in the previous examples andmodalities, admittedly more common in practice, the Assertion Model iscoerced upon the Apportionment sub-Model (with the latter supplyingvalue to the former), this process may be inverted at user and/or systemoption (or for any other reason that may arise in the relevant context),effectively requiring the creation of a new Assertion Model based uponthe structure of a selected Apportionment sub-Model.

Continuing to reference FIG. 13, composite functional block 1116, thepervasive collection of composite functionalities present in numerousfigures and described in associated descriptions and examples within avariety of contexts and variations, receives inputs as designated bycallouts Assertion Model(s) 11202 (an Assertion Model drawn from thecollection of N Assertion Model 13001), 11004 (a Reference Data Model)and an Apportionment sub-Model from the collection of K Apportionmentsub-Models 11001. As in previous figures, the output of block 1116 isshown as composed within a sorted and indexed collection of OutcomeModels referenced by callout 11304 (“Multiple Discrete Outcome Models—NAssertion Models projected across Single Reference Data Model using KApportionment sub-Models”) where the M Candidate Outcome Models arestored within data stores Candidate Outcome Model 1 12004 throughCandidate Outcome Model M 12005 and the Q Cardinal Outcome Models arestored within data stores Cardinal Outcome Model 1 12006 throughCardinal Outcome Model Q 12007.

As explained in connection with FIG. 12, the execution chain depicted inFIG. 13 may exist as a variation of the“single-Assertion-single-Reference-Model” modality of AssertionalSimulation, but as was the case in the“single-Assertion-multiple-Reference-Model” modality, in the presentcase (the “multiple-Assertion-single-Reference-Model” modality) thisexecution environment and in embodiments and contexts, the“single-Assertion-multiple-Reference-Model” modality may also, inembodiments, be implemented as a standalone execution modality or may bepresented to users (or system processes) as a type of discrete executionchoice or as an element within one or more larger execution sequences.

One example of the manner in which the“multiple-Assertion-single-Reference-Model” modality may be applied isexemplified by the application of Assertional Simulation (and thesupporting elements within a Platform 1000) to the evaluation ofinitiatives of all types in professional and business and governmentenvironments as well as in other instances, as cited in examples withinthis disclosure (including, for example, the use-case variationscentered about the bibliophile and even those illustrating the use ofAssertional Simulation by the volunteer organization). Note, however,that there may be other instances and circumstances and contexts inwhich the “multiple-Assertion-single-Reference-Model” modality may beapplied. In embodiments, for example, this modality may be parameterizedand invoked within the execution context of a different modality ofAssertional Simulation, as a type of conditional or looped branch,which, in embodiments, may produce one or more results and/or byproducts(spawned in the process of the derivation of these results) which may beused for: i) “standalone” evaluation and examination or analysis and/or;ii) which may be employed as variables (for example) within the contextin which it was invoked and/or; iii) may be defined as an informaticaggregate bounded by (and optionally associated with) the context inwhich it was invoked (using for example, one or more aspects of the APMindexing capabilities) and which may then be provided to and/or withinany of the enumerated DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces cited in thisdisclosure.

To illustrate how the “multiple-Assertion-single-Reference-Model”modality may be used in the evaluation or ranking of initiatives by auser or group of users, recall the previously described example whereina manager tested his views of efficacious deployment and allocation ofresources (his view of “ground truth”) by assembling an Assertion Model(as described through this disclosure) and parametrizing instances ofthe Apportionment sub-Model using DTS Subjectal Coercion and applyingthis collection to a selected Cardinal Reference Data Model, as outlinedin the previous example related to FIG. 12. But as noted in the earlierexample, this manager used various mechanisms within DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesto “perfect” the parametrizations within his collection of Apportionmentsub-Models and these methods, so far, embody an example of thedeployment of the “single-Assertion-single-Reference-Model” modalitysince these optimizations were derived based upon a single CardinalReference Data Model. But, as in the original example, suppose themanager wishes to examine and analyze a number of new initiatives to hiscurrent business, initiatives that require adding one or more new profitcenters. This proposition, in this non-limiting example, includes addingsuch things as new employees, additional equipment and increasedindirect expenses but also new revenue. Among the ways to accomplishthis in a DTS Platform 1000 and to apply the analytic and predicativeaspects of Assertional Simulation (as was briefly referenced in theearlier example), DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces may be used to initiate aninstance of the previously described “what-if” strategy where, in thiscase, the “what-if” procedure involves creating an alternate version ofthe Cardinal “ground truth” as embodied in a selected Cardinal OutcomeModel and the attendant Cardinal Assertion-Apportionment pairs. The newand additional elements that composed these initiatives (or, in thiscase, the costs and revenues associated with new profit centers) may beintegrated within a new version of the Cardinal Outcome Model and withinthe elements that would produce such an Outcome Model, including theAssertion-Apportionment pairs, which would be re-structured andre-parametrized. DTS provides systematic means to do this type ofanalysis, a contribution to the current art that simplifies and improvescurrent practices.

In this example, suppose the manager uses a “clone-and-modify” strategywherein the manager selects a particular Cardinal Reference Data Modelbased upon common acceptance that this particular Outcome Model issufficiently representative of the current consensus view of groundtruth in the organization. The manager may make and rename a copy of notonly the Cardinal Reference Data Model but of the collection ofinformation aggregates from which it was derived, including theantecedent Assertion-Apportionment pairs, as outlined in the foregoing.To inject the new initiative, the manager may invoke another variant ofthe DTS “clone-and-modify” strategy using DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces to selectan existing profit center within the now-cloned-and-renamed OutcomeModel and may duplicate this subset of interconnected nodes, may renameand re-parameterize the elements within the aggregation which, ofcourse, consists of the accounting-based elements associated with thatprofit center.

Specifically, in an expansion and in further detail of the earlierexample, the manager, using DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces, may identifyexisting groups of one or more informatic nodes within (one or more) aninformatic aggregation(s) (in this case, a single aggregate, thenow-cloned-and-re-named single Reference Data Model) that are related toand which define, in the native ontology of the accounting system (or invariations, as may be extracted using content-emergent analytic andextraction techniques, as described in the foregoing) the constitutionof a profit center (as defined and embodied in this instance within thenow-cloned Reference Data Model). This aggregation may include suchitems as revenue, direct and indirect expenses, employees and otherfactors related to the operation of the organization. Usingcontent-sensitive and content-responsive methods and procedures andinterfaces, this distributed aggregation of nodes may be duplicated andrenamed, and new values may be associated with each node, anaggregate-wide re-parametrization that reflects a version of themanager's vision of this new initiative. Note that in embodiments,employees may be separately selected and cloned that may not beassociated with the currently selected to-be-cloned profit center. Thus,for example, if that existing (“real”) profit center does not have anengineer of a certain type, one can be selected from elsewhere, cloned,re-named and re-parametrized, Moreover, in variations, a DTS Platformmay provide a library of employee-types that may be based upon theorganization's current employee spectrum, from which such additionalemployees may be selected and re-parametrized. In further variations,users and/or system processes may produce such synthetic entities usinga number of mechanisms, including operations that may be invoked to“make another version of John Smith and name her Jane Smith”. Note that,as discussed within this disclosure, any or all such nodes may haveattached ancillary information and these attached informatic structuresmay be optionally cloned and modified, as well.

This newly-spawned aggregation representing the new profit center maythen be included within the existing clone of the cloned Reference DataModel using one or more DTS-based (and/or DTS-enabled and/orDTS-accessible) and, in variations, dynamically andcontextually-assembled assembled content-appropriate, content-sensitiveand content-responsive methods and procedures and interfaces tointegrate the now-changed information throughout the cloned-and-renamed,and thus modified Reference Data Model. Using this new Reference DataModel, the manager may now create and parametrize a new set ofAssertion-Apportionment pairs and invoke the“single-Assertion-single-Reference-Model” modality to produce an OutcomeModel based on this version of the “what-if” scenario in which hisenvisioned initiative is actualized and embodied with existinginformation. embodied. In the“multiple-Assertion-single-Reference-Model” modality, this process maybe repeated with different re-parameterizations of the Apportionmentsub-Models until the Outcome Model produced using this cloned andmodified Reference Data Model comports with the manager's goals and thusreflects his view of an appropriate “ground truth”.

The manager may compare and contrast each Outcome Model throughinvocation of any number of DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces available within aPlatform 1000, including simple inspection using special two- andthree-dimensional inspection tools, deployment of analytic andstatistical routines as well as any other technique used for comparingand optimizing elements within discrete data sets. In embodiments, suchoptimizations may be executed using any of all of the looped iterativeand recursive optimization methods described and implied in thisdisclosure and other techniques not explicitly detailed here but haveutility in such applications.

But note that at any time (or upon realization of an optimizedparameterization of a set of Apportionment sub-Models), the manager mayretain these versions and repeat the entire exercise by, for example,adding several profit centers or by changing any relevant values withinthe cloned-and-renamed Reference Data Models. Thus, in this example, notonly might the manager invoke the“multiple-Assertion-single-Reference-Model” modality of AssertionalSimulation but may do so repeatedly. Note further, that, as in theearlier example, using statistical and analytic tools within DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces, the manager may project any of these Outcome Models forwardand backward, producing a time series of concatenated but syntheticrepresentations of the resources within an organization.

This example of the “multiple-Assertion-single-Reference-Model” modalityof Assertional Simulation shows another example variation of theDTS-engendered layered and multi-modal zero-sum/non-zero-sumcompetition, as outlined in previous examples. Note, however that inthis instance, the user sets up competing versions of Apportionmentsub-Model parameterizations as coerced upon a synthetic Reference DataModel that may be projected forward and backward and evaluated, asdescribed. By selecting a particular version of the Outcome Model fromthe synthesized collection as optimal (and thus, the antecedentAssertion-Apportionment pairs and other information), this Outcome Modelwins the zero-sum competition, the overarching game which subsumes thestacked series of non-zero-sum competitions. But in a further variationof this example, the manager may indeed create a time sequence ofprojected Outcome Models, but using DTS-based (and/or DTS-enabled and/orDTS-accessible) content-sensitive and content-responsive methods andprocedures and interfaces may additionally synthesize newAssertion-Apportionment pairs that associate with these derived Outcomesand may change all or part of the parameterizations within theseAssertion-Apportionment pairs. In advanced variations, suchre-parameterizations may be conditioned to reflect conditions that mayhave been present or which may have influenced original or “real”Assertion-Apportionment pairs, such as, for example, weather-related orseasonal factors.

Note that many of these aspects in this modality of AssertionalSimulation as with the other modalities, and as with many other elementsof the services and functionality provided within a Platform 1000, arecast within the overall context of the competitive, collaborative andcomparative environment that suffuses the DTS Platform. This pervasivepresence is depicted in multiple figures, descriptions and examples andis exemplified by composite block 1117 (entitled “Manage DTS Competitiveand Collaborative Simulation Environment with Optimization”) in FIG. 1and in various forms in other figures, as well. In the current example,apart from internal layering of zero-sum/non-zero-sum competitions, thiscompetitive aspect may be seen by including earlier examples whereingroups of managers compete for resources. In this instance, groups ofmanagers may competitively submit their views of which initiativesshould be pursued and the relative merits of different resourceallocations that should be associated with such pursuit. Thus, eachmanager may create and nominate their own Outcome Models based on eitheralternate initiatives or their view of the same agreed-upon and sharedset of initiatives (that is, initiatives that are agreed by the group torepresent the best possibilities). In the latter case, managers injecttheir judgement of an optimal version of what the “ground truth” oughtto be based given the inclusion of the initiative(s). Their submittedOutcome Models reflect their views of the configuration of the newprofit centers, including such things as how many new employees and ofwhat type should be added, what types and at what cost of capitalequipment might be required, as well as other details involved in a newinitiative as well as the proper re-parametrizations of the newlystructured Assertion-Apportionment pairs. Each manager may create andsubmit and prepare analysis of their alternate versions of the sameinitiative-laden Outcome Model, and the selected “winner” may bedesignated as Cardinal to be used in pursuit of the initiative (calledin DTS-terminology the “Cardinal Paradigm Outcome Model”) and indexed tothe associated Assertion-Apportionment pairs).

Note, however, that this Cardinal Paradigm Outcome Model (and all of theparameters within the associated Assertion-Apportionment pairs) may bepreserved and compared to the actual real-life outcomes, as with otherinstances of Assertional Simulation, thus spawning a variety ofdifferential and correlative analytic byproducts and opportunities, anyor all of which may be integrated and used in the creation andmaintenance of any or all elements in any of modalities of AssertionalSimulation. In embodiments, such differentials may be used in one ormore elements within the DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces represented withinthe in-App metrics functionality (as represented in composite functionalblock 1107 in FIG. 1 and other figures), capabilities that, inembodiments, pervade a Platform 1000. In this example, the differentialsmaybe compiled as a type of “predicted-versus-actual” analysis withinwhich plethora statistical routines may tease out factors which may beused by managers to improve their predictive abilities. Moreover, sinceall the Candidate Paradigm Outcome Models (and their progenitorialAssertion-Apportionment pairs as well as ancillary and supplementalinformation generated in the creation and analysis process) produced bythe manager may be preserved, such information may be included in theongoing analysis, providing not only additional insight and data pointsbut which may also serve as one measurement of a manager's performance.This is another example of the use of Assertional Simulation as apredictive analytic.

Another example of the utility of these modalities of AssertionalSimulation may be seen in application of DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces to theevaluation of external sales opportunities. In many environments, thereare multiple such opportunities (various new clients for a consultingfirm, for example) and, in embodiments, a DTS Platform 1000 may providecapabilities by means of which a potentially large number of suchopportunities (a common problem in these environments) may be reduced byapplying one or more modalities of Assertional Simulation in conjunctionwith ancillary DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures. As in the case of the integration andutilization within DTS Platforms 1000 of such commonly available toolssuch as, for example, analytic and data mining capabilities, othermethods commonly employed in sales-oriented environments may alsosupplement and/or augment the capabilities of Assertional Simulation andmay be employed in conjunction with any or all of the DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesand/or any or all of the extensions enumerated with examples anddescriptions within this disclosure and in various concreteimplementations of a DTS Platform 1000 and those which may be inferredby those experienced and skilled in these and related arts.

One problem commonly encountered in this arena is determining which setof potential targets among a large group represents the best use of thelimited resources any organization may expend on new businessdevelopment. In DTS-terminology, this problem is addressed by a novelconstruct implemented, in embodiments, as a multi-staged process thathas three optionally non-sequential components: a pre-qualificationstage, called “DTS Prospecting”, a middle stage called DTS Culling and afinal stage called DTS Initiative. The large number of potentialopportunities are addressed by the combination of DTS Prospecting (thepre-qualification stage) and DTS Culling (the intermediate stage) andends with DTS Initiative. In embodiments, DTS Initiative entails theapplication of one or more modalities of Assertional Simulation in whichone or more users create one or more ordered lists which reflect thatuser's particular view of the relative value of each instance, eachrepresentative of the judgements of different business and salesdevelopment personnel, though in variations, Assertional Simulationmaybe applied throughout the other stages. The assertion of the relativeefficacy and value of each opportunity is, of course, a version of theseminal principle of “perceived ground truth” in that each rankingrepresents not only a judgment-based evaluation of each opportunity butimplicitly defines the user-based perception of the broader situation orcontext as it is or as it should be.

In embodiments, opportunities in DTS Prospecting (the pre-qualificationstage) may be statically, conditionally and/or transiently segregatedinto one or more categories, according to many possible criteriaincluding properties that may be contained within the initiativesthemselves (such as business type or various measures of magnitude) orby the degree of completeness of information according to one or moreapplicable templates, or any other reason the managers may choose. Theselection of the applicable criteria, the parameterization of suchcriteria and the creation and maintenance of the templates as well asother elements that, in context may be related to, derived from andwhich may contribute to one or more results that ultimately causepotential initiatives being placed into “bins” may, in embodiments, useany or all of the standard and known methods applied to such schemes,but may also include any of the described DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces. DTSProspecting may additionally and in a supplemental manner apply one ormore elements within any of the various modalities of AssertionalSimulation to determine which potential initiatives should be in whichbins, and further which may be of highest interest. The combination ofstandard information categorization techniques with the novel modalitiesof Assertional Simulations (and ancillary, related and supportiveoperations) is called in DTS-terminology “DTS Culling”, an intermediatestage in DTS Prospecting, typically but not exclusively applied to fewernumbers of possible solutions.

In variations of DTS Culling, users and or/system processes may employstrategies (either as group or individually, effectively in competition)by means of which potential initiatives may first be sorted intocategory bins (as in the foregoing) and may then be subject to theformalized view or “ground truth” held by a user (or group of users) ofthe relative value of each initiative. But since not all targets havethe same amount of information in the same format from the same domain,any of the DTS-based (and/or DTS-enabled and/or DTS-accessible)content-sensitive and content-responsive methods and procedures andinterfaces described in the foregoing as may be applied to disjoint andnon-continuous informatic structures as well as the methods andtechniques associated with DTS semantic de-referencing (and othermethodologies outlined in this disclosure) may be applied to harmonizethese randomly and possibly variably disjoint and eccentric aggregates.Such operations may be supplemented by practices such as subtractivefiltering (in order to eliminate (or delay evaluation of) targets thatpossess less than acceptable degrees of complete information), patternmatching as well as system-based, system-enabled and human-basedinformation manipulation and intervention using appropriately configureduser and system interfaces.

Thus, in embodiments, in the course of the optionally iterativeexecution of one or more elements in the DTS Culling stage employingDTS-based (and/or DTS-enabled and/or DTS-accessible) content-sensitiveand content-responsive methods and procedures and interfaces, eachsurviving potential initiative may be indexed to an Outcome Model (inimplementations, along with other ancillary information) that may beassumed to be complete to the degree possible but may not, in all casesbe fully representative of each target to the degree that may berequired to obtain a fulsome or meaningful ranking. Thus, the target maybe subjected to repeated cycles of DTS Prospecting, thepre-qualification stage where target information is assumed to beprimordial and thus must be subject to grosser processing and thenre-submitted to DTS Culling, Ultimately, the goal is to reduce thenumber of targets with as-complete-as-possible information so thatmodalities of Assertional Simulation (as well as other methods) may beeffectively applied, although, in variations, modalities of AssertionalSimulation may be conditionally and partially invoked onless-than-complete information. The goal of each reapplication of thesenominally-concatenated but not-always-sequential stages is to obtain asmaller number of targets which have enough information to be nominallyeligible for inclusion within Assertional Simulation computations. Thus,in the context of this disclosure, one or more modalities of AssertionalSimulation may be initiated in the final stage (DTS Initiative), and anAssertion-Apportionment pair may be nominated and parametrized using anyor all of the means enumerated in the foregoing (and others that may beapplicable) and thus applied to a set of possible targets, creating acollection of Outcome Models.

In one variation of the application of Assertional Simulation that maybe applied in either of the stages (DTS Prospecting or DTS culling),each target may be compared to a synthetic version of an “ideal” target.In this instance, one or more users and/or system processes create oneor more Outcome Models representing the “ideal” target where, inembodiments, there may be many such ideals formulated by different usersand/or representing different categories or target types. UsingDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces, each target may be seen as an Outcome Modeland may be ranked based upon its degree of correlation to the idealOutcome Model. This approach itself is not dissimilar to other commonlyemployed correlation-based evaluation tools, but within DTS, the noveltools of Assertional Simulation may be applied.

Specifically, one strategy may be to deploy the“multiple-Assertion-multiple-Reference-Model” modality of AssertionalSimulation depicted in FIG. 14. In this non-limiting example case, thereare N Reference Data Models (which are, as described in the currentuse-example, the potential initiatives or targets) depicted as reposedwithin data store Reference Data Model 1 12000 through Reference DataModel N 12001 and indicated by callout Two or More Reference Data Models12001, noting that in the present example, there are clearly more thantwo such models. Note that in this case, however, one which is exemplaryof the “multiple-Assertion-multiple-Reference-Model” modality ofAssertional Simulation, there are many Assertion Models (data stores13000 and 13002 designated by callout 13001, as described in theconnection with FIG. 13, each with one or more associated Apportionmentsub-Models (data stores 11100A and 11100B designated by callout 11001,as described in the connection with FIG. 11 and other figures). Thecontrol mechanisms that execute the selection of which Reference Modelupon which to apply which Assertion-Apportionment pair(s) (blocks 13003,12003 and 11002) operate as described in other examples and as depictedin other figures wherein these operations may be user-initiated andcontrolled and/or maybe executed by means of full or partial systemcontrol, where one or more elements of such control are executed bymeans of methods and procedures depicted within composite functionalblock 1104 and subsumed within control flow 1111 as shown FIG. 1 andother figures. In any such instances, any of all of the looped,iterative and recursive operations in any of the configurations andmodalities may be applied here where such techniques are describedthroughout this disclosure. As in previous figures and examples anddescriptions, blocks 1102, 1116 and 1103 receive input from prior stages(shown as subsumed within composite data flow 14000 entitled “AllModels” and output the resulting Outcome Models to either block 1119(“Manage Selection, Formatting, Processing of Models forDTS-based/DTS-enabled Analysis and Visualization 1119”) which subsumesDTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces that enable user- and/or system-drivenapplication of visualization and analysis. In this case, such analysismay be applied to the Outcome Models, but as discussed in thisdisclosure, this capability is pervasive through a Platform 1000 and maybe invoked any time and in any context upon any information.

Note that in FIG. 14, the Outcome Models are shown as subsumed withindata stores Candidate Outcome Model 1 12004 through Candidate OutcomeModel M 12005 and data stores Cardinal Outcome Model 1 12006 throughCardinal Outcome Model Q 12007, but in the present example, as well asin others, there may be only Cardinal Outcome Models and no CandidateModels. This is because in the current case, Outcomes are rankedrelative to one another in a non-zero-sum game, a variation on thelayered game-theoretic characteristic that, as described, pervades DTSPlatform 1000.

Continuing the present example of the final stage of the evolutionprocess, each user in a business development environment may apply theirown evaluation criteria, parameters that are embodied in theirrespective Assertion-Apportionment pairs. Each user then applies theirformalized judgments to each instance of Reference Data Models from thecollection 12001 through the execution described in the foregoing. Inembodiments and in some applications, there may be a number ofApportionment sub-Model parameterizations applied to different sectionsof the Reference Data Models, and in variations, there may be differentAssertion-Apportionment pairs applied, as well (or instead, based uponapplication of one or more content-sensitive and content-responsivemethods and procedures and interfaces within the execution chain.

In all cases, each user ultimately ends up with an ordered list, rankingthe relative value of each potential initiative as embodied in aReference Data Model, and any or all previously described DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces may be applied to construct and maintain any element of theexecution chain that produces these results. Note as well that invariations, there may be many such ranked lists for each user based ondifferent criteria and different parametrizations within or associatedwith one or more Apportionment sub-Models. But note that in someinstances, instead or in addition, such re-orderings or permutations ofthe ranking lists may result from application of one or more analytic,pattern recognition and other techniques (such as may be found in datamining applications) as made available through DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures andinterfaces, resources that, as mentioned, are available in any contextthroughout a DTS Platform.

Thus, in the current example, of the many lists that may be generated bythe business and sales personnel participating in this ranking process,in general, one list is selected as Cardinal thus indicating whichpotential initiatives are most worth pursuing and which have thegreatest chance of success. Thus, this example and this modality ofAssertional Simulation (“multiple-Assertion-multiple-Reference-Model”)exemplifies a variation of the layering of non-zero-sum results withinan overall zero-sum outcome where, in this instance, there may be atleast two non-zero-sum competition types: the application of AssertionalSimulation in the non-zero-sum modality described in the accountingexample with respect to apportioning within a child set in a hierarchy;and the overall ranking of Outcome Models where, as mentioned each usermany create many such non-zero-sum results. But also, as noted, inimplementations and in some contexts, one such non-zero-sum ranking isselected, thereby designating a zero-sum winner.

This exercise may be repeated at intervals, and since, in general, allthe information generated in the course of these operations ispreserved, the overall Shannon entropic richness within the platform isincreased, an aspect of DTS Platforms 1000 that, as described, may beleveraged by such capabilities as machine learning, pattern recognitionand other AI-related functionalities. Note, that as in the previousexample, metrics and analysis may be extracted comparing how wellcertain selected initiatives actually perform, again comparing projectedto actual performance. In one variation, one or more elements of suchactual-versus-predicted metrics may be used to optimize both thestructure and parametrization of Assertion-Apportionment pairs but alsothe structure of Reference Data Models, where such information mayreveal which information is more important and in what manner thanothers. Moreover, such information may be used in earlier stages tooptimize the DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces that execute that functionality,(including interpreting such information to adjust any or all of theelements within the applicable dynamically invoked content-sensitive andcontent-responsive methods and procedures and interfaces). In addition,such metrics may be used to improve and otherwise modulate suchsynthetic or derived “ideal” representations as described in theforegoing, where use and dynamic modification of such synthetic entitiesmay be invoked in many contexts throughout a DTS Platform 1000. In allcases, such information may be widely used both within DTS Platforms1000 (in the previously referenced in-App metric capabilities, forexample) as well as in external systems through appropriate DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces. This is another example of the use of Assertional Simulationas a predictive analytic resource (DTS Subjectal Prediction).

Returning to FIG. 1, and DTS Assertional Simulation Operations 1103subsumes compound function block Manage DTS Competitive andCollaborative Simulation Environment with Optimization 1117, depicted asadjacent to block 1116. This arrangement graphically conveys thefunctional relationship that may, in embodiments, exist between themethods and procedures and interfaces within each respective block, acontextual interoperation between these computational constituents. Butas with other graphical conveniences mentioned in this disclosure, sucharrangements should not imply any type of mandatory interconnectionbetween 1116 and 1117, nor between any of the respective constituentelements, nor any sort of required order of execution or precedence. Inall events, as is the case with blocks 1116 and 1117, this represents acollection of functionally similar but separable (and in embodiments)distributed methods and procedures and interfaces unified in manycontexts by a focus upon the execution and facilitation of the variousand pervasive competitive, collaborative and comparative functionalitywithin a Platform 1000. Note, however, that, in embodiments, thesecollections of methods and procedures and interfaces may also containadditional functionality as described in any or all aspects of theforegoing, including, in a non-limiting example, the interfaces andentity management capability within and as may (in embodiments) beassociated with or other accessible to APM and RAMS System Capabilityand TCM (Topical Capability Mediation), any or all of thecontent-sensitive and content-responsive methods and procedures andinterfaces previously described as well as any of the elementsassociated with the execution chains surrounding and related toAssertion Simulation and the attendant Models and ancillary structures.

Note, however that while some execution blocks described in theforegoing may seem nominally unconnected to the competitive,collaborative and comparative aspects of Assertional Simulation and thebroader capabilities of a Platform 1000, in the execution environmentscontained within 1116 and 1117, in embodiments and as impelled bycomputational-, user- and system-driven intentionality and contexts,such capabilities may provide (in a non-limiting example) one or morepredicate variables, intermediate and ancillary informatic structuresand other elements that may be used within the scope of the competitive,collaborative and comparative functionality. This competitivefunctionality has been cited in examples and descriptions in theprevious paragraphs and, as described represents a pervasivecharacteristic throughout a Platform 1000, elements of which that may beinvoked and executed in many contexts and at any time. Given thisinteroperability and system-context independence, block 1117 isappropriately titled “Manage DTS Competitive and CollaborativeSimulation Environment with Optimization” and should be understood asbroadly depicting collections of DTS-based (and/or DTS-enabled and/orDTS-accessible) content-sensitive and content-responsive methods andprocedures and interfaces that may be dynamically assembled to enable,to enhance and, in general, to facilitate this operational capability,providing direct and indirect support of the pervasive capability in aPlatform 1000 implementation to foster, encourage and executecompetitive, collaborative and comparative functionality. As noted,however, in embodiments, many such constituent elements may also performother non-related functions, such as, in a non-limiting example,providing computational assistance for variables that may be used withinthe in-App metric and analysis capabilities (as described throughoutthis disclosure and as depicted in composite block 1107 in FIG. 1). Butnote, as well, that in a manner similar to block 1116, block 1117combines related but, in some cases, contextually discrete functionalityby including optimization within its composite functionality. As notedin the foregoing text, optimization within a DTS Platform 1000 and inconnection with one more operations related to and/or assisting in theexecution of Assertional Simulation (and/or ancillary operations) may,in embodiments, compose diverse, varied and complex operations,including (but not limited to) those outlined in connection to blockManage Looped, Iterative and Recursive Operations 1105 and controlsignal/data flow Optimization Control 1108B.

FIG. 1 combines this functionality since though discrete in somecontexts, the competitive, collaborative and comparison environment hasimportant commonalities within the optimization capabilities providedthroughout Platforms 1000. This may be clear at a colloquial conceptuallevel since one objective of competitive, collaborative and comparisoncapabilities is to attempt (by whatever means) to optimize some set ofresults (Outcome Models, for example) or to select which results among acollection of is optimal, where in many cases, “optimal” is defined bythe current computational- user- and system-driven intentionality andcontexts. But note that in technical and implementation terms, thegraphical interconnection is more mundane and practical: in embodiments,users and system processes most often (but not exclusively) initiate andexecute many of the optimization operations in the computational andinterface-based context of the competitive, collaborative and comparisonenvironment, and in this sense, it is appropriate to combine thisfunctionality in the context of the high-level teachings depicted inFIG. 1. But note, again, as well, that this combination does not implydependence, priority or any other connection requirement.

Continuing within integrated compound functional block 1103, compoundfunction block Select and Store Cardinal and Candidate AssertionalSimulation Outcome Models 1118 represents, in embodiments and in manycomputational contexts, a less complex and variegated aggregation offunctional capabilities than those depicted as subsumed within 1116 and1117 (and 1112, 1113 and 1114 within compound function block 1103), orrepresents, at least, a collection that may have few capabilities with alesser degree of Platform-wide interconnectedness, but one that is noless important. This functionality has appeared in previously providedexamples and descriptions and the context-based assembly of thiscollection of DTS-based (and/or DTS-enabled and/or DTS-accessible)content-sensitive and content-responsive methods and procedures andinterfaces. Note that, as in the previous explanations, the graphicaladjacency shown in FIG. 1 should not imply any order of executionalprecedence and that the methods, procedures and interfaces provided by1118 should be understood as available at any time and by other elementswithin 1103. Such flexibility may be seen in the depiction of thebidirectional flow 1120 (as discussed in the foregoing text), and theselection and storage functionality should be understood as prevalentbut, of course subject to the strictures exerted by the elements withcontrol flow RAMS and TCM Control 1109.

Finally, within compound function block 1103, composite functional blockManage Selection, Formatting, Processing of Models forDTS-based/DTS-enabled Analysis and Visualization 1119 is connected inthe same pervasively-available and random-access fashion as othersystem-wide capabilities and should therefore be understood here asavailable to other functional blocks as required by any operational andfunctional context within present in embodiments of DTS Platform 1000.The DTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces within block 1119 have described throughoutthis disclosure and in many examples and use-cases. The suite offunctional capability within composite block 1119 has many practical andvaried applications, including but not limited to the application ofnon-destructive analytic routines, such as, for example, situationswhere versions of Reference Data Models may be projected forward byapplying linear regression algorithms upon certain elements within thedata and recalculating and comparing results across differentiterations. In this context, as one example, as described in previousexamples, such results may be fed-back into previous stages in thederivation of Reference Data Models, even integrating new information asoutlined in previous discussions. It will be appreciated in light of thedisclosure that the collective functionality within composite block 1103enhances and extends the usefulness of DTS Reference Data Models, notonly once composed but in the composition and assembly process itself.

In addition to the capabilities enumerated in other sections of thisdisclosure, composite block 1119 may also, in embodiments, provide abroad and highly configurable content-sensitive and content-responsivesuite of methods and procedures which, as with other aspects of suchcollections of methods described in the foregoing, may be DTS-based(and/or DTS-enabled and/or DTS-accessible) and may in context beassembled dynamically to facilitate these evaluations and comparisons.Thus, any number of tools may be available to users and system processesincluding (but not limited to): i) visual inspection by one or moreusers through any number of DTS-based (and/DTS-enabled) numeric andgraphical interfaces; ii) application of DTS-based (and/DTS-enabled)analytic, statistical and other mathematical algorithms which may alsobe deployed for standalone application but which may also integrate inthe context of a broader optimization effort (where such techniques maybe employed in an execute once configuration but which may alternativelyparticipate in iterative and self-modifying and/or adaptive loops, as inthe foregoing); iii) application of DTS-based (and/DTS-enabled) patternrecognition and rule-based and inference-based procedures to discoverrelationships within the subject data and, through any of the meanscited herein, make modifications to the subject data using DTS-based(and/DTS-enabled) methods; iv) application of DTS-based (and/orDTS-enabled) techniques typically associated with machine learningenvironments (as described in the foregoing and in other sections ofthis disclosure), including (but not limited to) techniques that may beused to operate upon one or more elements within the subject data basedupon (or executed as a result of) relational or compositionalinformation that may be discovered on the basis of content-emergentand/content-based inferences and/or upon information otherwise embeddedwithin the subject data information and which may be mined from suchsubject data and which may have been extracted from or inferred from(using techniques typically used in related disciplines), and which maydeploy content-responsive optimization methods. As described throughoutthis disclosure and as illustrated in various examples and graphicaldepictions, such evaluations and comparisons may be applied to (one ormore) Models (or sections therein) in conjunction with and/or inaddition to and/or as a part of any or all the techniques cited in theforegoing and, additionally, as may be associated with DTS loopedrefinement as embodied in iterative/recursive looping 1115, as describedin the foregoing.

FIG. 2 provides an alternate representation of the functionality shownin FIG. 1 and much of the same functionality described in the foregoingdescriptions, examples and graphical depictions are shown here.Composite functional block 1200 (“Execute Projective AssertionalSimulation—Create and Manage Candidate Reference Data Models, AssertionModels and Apportionment sub-models and Outcome Models 1200”) inconjunction with previously described block 1117 is shown in the contextof its interconnection with other functions as may be present inimplementations of Platforms 1000. In this representation, informationflows 1201 and 1202 entitled “Candidate Models” and “Cardinal Models”,respectively, as bi-directionally connected to both blocks 1200 and 1117which conveys the manner in which the DTS-based (and/or DTS-enabledand/or DTS-accessible) methods and procedures and interfaces withcontent-sensitive and content-responsive methods and procedures andinterfaces within each block may pass information to these Models in avariety of computational contexts. As in other example representationsand descriptions, and as a reflection of the functionality throughPlatforms 1000, control/data flow 1111 is shown as connected to all thefunctional blocks within FIG. 2. Note, however, that the absence of sucha graphical representation should not imply the lack of interconnectionof any of the components within a given block but is merely aconvenience when the availability of such control and data isunderstandable from the context disclosed herein.

The previously referenced and described composite functional block 1117provides output to data stores data stores Candidate Reference DataModels 1206, Candidate Assertion Models 1207 and Candidate Apportionmentsub-Models Data Models 1208 through bi-directional data flow designatedby callout 1201 and to Cardinal Reference Data Models 1209, CardinalAssertion Models 1210 and Cardinal Apportionment sub-Models Data Models1211 through bi-directional data flow designated by callout 1202. Asindicated throughout this disclosure, block 1117 and theserepresentative data stores are depicted here to convey their ubiquityand flexibility in the contexts and applications described in thisdisclosure and in other such applications that may be enabled within orby one or more components within a Platform 1000. In the context of thecurrent representation, and as may be seen by the functionality withinthe DTS-based (and/or DTS-enabled and/or DTS-accessible) methods andprocedures and interfaces within block 1117, including but not limitedto those described and illustrated in this disclosure (such as, forexample, the variety and variations made possible by thecontent-sensitive and content-responsive methods and procedures andinterfaces that, in embodiments, may be assembled in computational andinformation-derived contexts), the output of block 1117 is a compoundcontrol/data flow 1203A (entitled “Outcome Models 1203A”) from whichinformation is subsumed within data stores Cardinal Outcome Models 1204and Candidate Outcome Models 1205. Note that a data/control flow permitsthese Outcome Models to be resubmitted for reconsideration (orre-competition) and may be made available for any other reason to otherfunctional blocks throughout Platform 1000, an interconnectionexemplified by compound control/data flow 1203B (entitled “Re-competeCycles 1203B”).

Compound functional block 1119 (“Manage Selection, Formatting,Processing of Models for DTS-based/DTS-enabled Analysis andVisualization 1119”), also previously described in various examples anddescriptions within this disclosure (and as depicted in FIG. 1 and otherfigures) receives the compound control data flow signals 1213 (entitled“Selected Outcome Model(s) 1213”) from data stores 1204 and 1205 butalso information from data stores 1206-1208 (representing the collectionof Candidate Models and from data stores 1209-1211 (representing thecollection of Candidate Models and Cardinal Models, respectively, asdescribed in the foregoing) depicted as subsumed within compoundcontrol/data flows 1212 (entitled “Selected Candidate Models 1212”) and1214 (entitled “Selected Cardinal Models 1214”). The access andselection mechanism by means of which such selections may be managed andexecuted by such capabilities as user and system process interfaces arerepresented within compound control/data flow 1111 but are administeredand modulated within possible constraints imposed by methods andprocedures and interfaces within and associated with APM functionalityas well as the mediation mechanisms of access and system operationalcapabilities within and associated with RAMS. These data/control flowsare also subsumed within compound control/data flow 1111 where suchaccess is dynamically and contextually administered as described anddepicted in figures throughout this disclosure.

Note, however, that in FIG. 2, more detail is shown depicting additionalbut non-limiting example outputs from block 1119, compound control/dataflows 1215 (entitled “Selected DTS Simulation Model(s) 1215”), 1216(entitled “DTS-based and DTS-enabled DTS Analytic Data 1216”), 1217(entitled “DTS-based and DTS-enabled DTS Graphics and Visualization1217”). These control flows, in embodiments, available throughinterfaces throughout a Platform 1000 depict the depth and breadth ofthe analytic and visualization capabilities that may be employed byusers and system processes, capabilities that have been elucidated inprevious exemplary descriptions and examples and referenced with figuresthroughout this disclosure. This functionality is expressed in FIG. 2 assubsumed within compound control/data flows 1218 (entitled “Selected DTSSimulation Model(s) 1215”) and presented to the compound functionalboxes 1219 and 1220 (entitled “Manage, Distribute and Store DTS-basedand DTS-enabled Analysis, Data and Visualization 1219” and “Format,Store, Distribute and Export DTS Data, Analytic and Data VisualizationReports 1220”, respectively). The DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces these compositeboxes represent include (but are not limited to) capabilities to access,manipulate and index the numerous instances of DTS-facilitated analysisand graphical representations and capabilities that permit users andsystem processes to create, manage, format, index and provide access toDTS-created and DTS-facilitated reports. Note that such capability iscommon in many parts of the data analytic arena and many applicationsand services provide a wide variety of functionality and capability indirect and indirect support of report and presentation preparation. TheDTS Platform provides its own suite of such capability, most especiallyin embodiments that reflect particular needs that may derive from andmay be a consequence of the novel capabilities it provides. But, inembodiments, Platforms 1000 also provide the ability to integrate suchnon-DTS capability by means of such facilities as API's, methods andprocedures and interfaces supporting direct and indirect import andexport of information and other control mechanisms and data transferintegration techniques.

FIGS. 3 and 4 provide example representations and additional details fora collection of ancillary and support functions present in embodimentsthat augment and extend the operation and capabilities of the methodsand procedures and interfaces surrounding various modalities ofAssertional Simulation, including such related platform-widefunctionality as the competitive, collaborative and comparativecomponents, the plethora of analytic and optimization services and othercomputational components provided within a Platform 1000 and describedwithin this disclosure (and others which may be achievable by thosefluent in these and related arts). Such ancillary and enhancementservices and features extend throughout a Platform 1000 and thefollowing enumeration of examples of these adjuncts and assistiveelements should not be considered exhaustive nor fully comprehensive,but rather, as providing non-limiting exemplifications of the many waysthat the core functionality of Assertion Simulation may be extended andenhanced, including (but not limited to) the various AssertionSimulation modalities of the, the computational and informaticbyproducts, transient states and transformed forms which may be spawnedin the course the execution of any of the modalities of AssertionalSimulation, and the concomitant (but in embodiments, optional) skein ofAPM indexing and RAMS mediation facilities. These ancillary operationsand extensions may be applied in context throughout a Platform 1000 and,in embodiments, are designed to be widely accessible for applicationwithin many user- and system-based functional blocks.

FIG. 3 provides an example illustration of the functional process andexecution flow associated with the DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces that compose themain ancillary capabilities that, in embodiments, may be applied toReference Data Models. Noting at the top of FIG. 3, two data sources(Internal and External Data Sources 1300 and Current Reference DataModels, Assertional Models and Apportionment sub-Models 1301 providedata flow into the contiguous, unified blocks Manage Internal UserControl, Interfaces and External Data and API Access 1104 (previouslydescribed in the foregoing) and Manage Selection and Acquisition of New,Supplementary and Ancillary Reference Data and Data Sets 1177.Additional control of this data and access is supplied by means thepervasive control signal Platform-wide Controls and Interfaces 1111.Data signal Selected Multi-Domain Data Sets 1304 provides the result ofsuch operations (1104 and 1177) to compound function block Structure,Assemble, Filter, Normalize, Re-Reference Multi-Domain Data into HybridDomain DTS Reference Data 1180 (described in various descriptions andfigures). FIG. 4 depicts processes that may be applied to Internal andExternal Data Sources 1300, Cardinal and Candidate Assertion Models andApportionment sub-Models (1300 and 1209, 1210, 1206 and 1208). As shownin this example representation, functional block Select ExistingCardinal and Candidate Assertion Models and Apportionment sub-Models andSelect Additional Data 1400 controlled by means of composite controlsignal 1111 select data from previous cited data stores (1209, 1210,1206 and 1208) as well as from Internal and External Data Sources 1300,also cited in the foregoing (and shown in FIG. 3). Furthering thisexample processing flow, the composite functional blocksCreate/Modify/Maintain Assertion Model Structure 1401 andCreate/Modify/Maintain Apportionment sub-Model Values 1402 interact withRevised Candidate Assertion Models and Apportionment sub-Models 1403from data stores 1209, 1210, 1206 and 1208. As shown in the variationdepicted in FIG. 4, therefore, data from data stores 1209, 1210, 1206and 1208 as well as from 1400, 1401 and 1402 via Revised CandidateAssertion Models and Apportionment sub-models 1403 may be presented tocomposite box Manage Assertion and Apportionment Models 1404. This blockconsolidates (again for pedagogic purposes) functional blocks IndexAssociated Information to Assertion Model Structure 1405, Permutation:Clone, Filter, Scale and Modify Assertion Model Structure 1406, AttachAncillary Data Information to Assertion Model Structure 1407, IntangibleData: Attach & Reference Intangible Information to Assertion ModelStructure 1408. Note that since most of the operations and capabilitiesrepresented in most of the functional blocks in FIGS. 3 and 4 have beendescribed and represented in various figures and examples throughoutthis disclosure (except for 1308-1310 and 1405-1408 and data storesEnhanced Candidate Assertion Models 1410 and Enhanced CandidateApportionment sub-Models 1411), and since these data stores andcomposite functional blocks retain their earlier titles and annotations,unless otherwise noted, in the following description, these previouslydescribed blocks as shown within this example process and execution flowoperate with substantially the same functionality as in other citedinstances. Note also that the functionalities depicted within FIGS. 3and 4, as they apply to Reference Data Models (FIG. 3) and to AssertionModels and Apportionment sub-Models (FIG. 4) operate in similar fashionand the following explanations and descriptions may combine thesefunctions without loss of specificity as may apply to the appropriateDTS Models, and without loss of general applicability to other contextsthroughout the services provided within a Platform 1000.

Finally, note that blocks 1308-1310 and 1405-1408 are depicted ascomposited but adjacent collections of interconnected functionalities,and this is intentional as well as functional and pedagogical. TheDTS-based (and/or DTS-enabled and/or DTS-accessible) content-sensitiveand content-responsive methods and procedures and interfaces subsumedtherein are, in embodiments, highly interconnected and share many commonmethods and interfaces and modes of operation, though, as mentioned,specific differences may indeed surface in practice and in context asthese methods are applied in different DTS Models. Therefore, as inother such presentations and descriptions throughout this disclosure,this adjacency should not, in itself, imply or specify any particularcomputational sequences, any particular mandated executionalarrangements nor any required order of execution, and neither shouldthey specify or imply any required or particular order of precedence(unless otherwise specified). That is, in implementations and variationsof DTS functionalities and DTS-enabled capabilities, any or all of theseprocedures may be arranged and interconnected as specific circumstancesand context may require but which also accomplish the same functionalgoals. Thus, for example, functional blocks 1308 and 1310 may share somefunctionality and may interact with and share control, interfaces andinformation as required, though such interconnection may not be shownhere.

Referencing FIG. 3 and FIG. 4, composite functional blocks 1308 (“Indexand Associate Reference Data Sets to Associated and AncillaryInformation 1308”), 1309 (“Permutation: Clone, Filter, Scale and AugmentReference Data Sets 1309”), 1310 (“Index, Scale, Augment and ModifyReference Data Sets with Intangible Data 1310”) and their FIG. 4equivalents 1405 (“Index Associated Information to Assertion ModelStructure 1405”), 1406 (“Permutation: Clone, Filter, Scale and ModifyAssertion Model Structure 1406”) operate in substantially the samemanner. This collective functionality makes available, singly or in anycombination and in any sequence, a suite of variegated content-sensitiveand content-responsive supplementary capabilities, any of which may beconditionally applied based on any system-based or user-referencedvariable or state. These capabilities may be applied to any or all ofthe computation-produced products and system processes throughout aPlatform 1000 as may be generated, modified, transformed or otherwisemutated. Such additions of and changes to existing computation productsand system processes may result from (or may be implied by or may beinferred based upon) execution of any or all of the DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesdescribed in this disclosure and within variations and as may be presentin concrete implementations.

The composite blocks 1308 and 1405 (FIGS. 3 and 4, respectively)represent an adjunct to the APM indexing capabilities described in theforegoing. Recall that, in embodiments, APM indexing is pervasivethroughout operations within a Platform 1000 and has been cited as mostoften present in certain process structures such as interfaces withininterconnected program elements. But elements that execute and manageand maintain the extended associational capability cited throughout thisdisclosure, and which make such indexing available as needed throughoutthe platform may, in some contexts be specific to one or more elementswithin DTS Models and related to derivative structures (as described inthe foregoing). As noted in earlier examples and descriptions, the novelcombinatorial and permutational capability that may be invoked (and isoften an important characteristic) in modalities and variations ofAssertional Simulation also spawn and mutate versions of associatedinformation, and these are often defined and relevant to one or morenodes or groups of nodes within DTS Models. In many instances, this isnot only context and DTS-model specific but must also be integrated intothe variations associated with different content types, internodalstructures and content-related domain-based differences, as described inthe foregoing. Thus, functionality adjunctive to APM (as describedpreviously) are presented as subsumed within blocks 1308 and 1405,providing an interoperable suite of DTS-based (and/or DTS-enabled and/orDTS-accessible) content-sensitive and content-responsive methods andprocedures and interfaces.

Blocks 1308 and 1405 provide the ability for users and groups of users(and, in embodiments, system processes and groups of systems processesas well as discrete aggregates of information) to mandate theinstantiation of supplementary information, to initiate association withsuch information and to execute logic associated with such informationwith respect to all information within the relevant DTS Models. As aconsequence, for example, specific Reference Data Models (and anyattached or associated data) may be indexed to a specific user or groupof users (and, in embodiments, system processes and groups of systemsprocesses as well as discrete aggregates of information) where suchownership imputed by operations within block 1308 may be executed,modified, and otherwise maintained by means of interfaces and proceduresas described in the foregoing according to the information withincontrol/data flow 1111, also as described. Further, as with otherprocesses, such ownership imputed by operations within block 1308 (andin the case of Assertion-Apportionment pairs, block 1405) may operateaccording to limitations and parameters imposed by RAMS access andtransaction mediation, as described throughout this disclosure. Finally,ownership imputed by operations within blocks 1308 and 1405 (and betweenother functional blocks as in the foregoing) may employ any aspect ofDTS looped, iterative and recursive techniques and optimizationmodalities, as referenced previously.

Moreover, the composite blocks 1308 and 1405 may, in embodiments, indexand associate one or more elements within the respective DTS Models toand with other associated and ancillary information not associated withthose DTS Models and which may be referenced to other users and groupsof users (and, in embodiments, system processes and groups of systemsprocesses as well as discrete aggregates of information). Thiscapability permits the instantiation and maintenance of both informationstores and procedural methods and processes independently of a ReferenceData Model or an Assertion-Apportionment pair but such that these datastores may be indirectly referenced to one or more specific elementswithin such Models. This associated and ancillary information mayoptionally also be indexed to (and thus owned by) one or more usersand/or groups of users (and, in embodiments, system processes and groupsof systems processes as well as discrete aggregates of information). Butnote that such ownership of such associated and ancillary informationmay be conditional in that a particular DTS Model (and thus any attachedor associated data) in the original or some combinatorically spawned orpermuted state, may also be indexed to that user or group of users.(and, in embodiments, system processes and groups of systems processesas well as discrete aggregates of information). Thus, thecontent-sensitive and content-responsive methods and procedures andinterfaces within blocks 1308 and 1405 provide additional indexing ofassociated and ancillary information based upon permuting anddynamically changing ownership as executed by the APM interfaces and theRAMS access control and mediation, a capability that may arise and maybe applicable in cases and in contexts that may require specialmediation not only within the control purview provided by RAMS but, invariations, using purposed user and system interfaces.

The composite blocks 1309 and 1406 (“Permutation: Clone, Filter, Scaleand Augment Reference Data Sets 1309”, and “Permutation: Clone, Filter,Scale and Modify Assertion Model Structure 1406” in FIGS. 3 and 4,respectively) represent system-wide and pervasive capability thatprovides mechanisms by means of which a DTS Platform 1000 may executethe extensive and detailed combinatorial and duplicative functions asdescribed in connection with various modalities of AssertionalSimulation but which may be applied throughout the operations of thePlatform. These include the variations of permutational operationsexemplified by the described contexts as “clone-and-modify” operations,the suite of DTS-based (and/or DTS-enabled and/or DTS-accessible)content-sensitive and content-responsive methods and procedures andinterfaces that may be invoked to execute duplicates (or clone) one ormore elements within a DTS Model (or other aggregation of information)or sections thereof, where such sections may not necessarily becontiguous, nor necessarily drawn from a single version of a given dataelement or DTS Model. In variations, such cloned versions (or sections,as described) may be indexed by DTS using the content-sensitive andcontent-responsive methods and procedures and interfaces within blocks1405 and 1408, thereby conferring a unique logical (and possiblyphysical) identity that may preserve ownership provenance and relationto those newly-spawned objects or which in some instances also permutessuch associations.

The interconnected suite of DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces subsumed withincomposite blocks 1309 and 1406 operates in conjunction with thefunctionality within composite blocks 1309 and 1406 to provide users andgroups of users (and, in embodiments, system processes and groups ofsystems processes as well as discrete aggregates of information)extended combinatorial and permutational capability combined with thedetailed content-sensitive and content-responsive and contextuallydynamic indexing and provenance-preservation functionality previouslydescribed. These combined capabilities include but are not limited tofunctionality wherein: i) (one or more) elements within duplicatedobjects (as in the foregoing) may be optionally (reductively) filteredand/or elided (and/or selectively eliminated) and/or augmented such thatthe duplicated objects(s) upon which such filtering operations may beapplied may also be indexed, thereby also attaining a unique logical(and possibly physical) identity distinct from the originalprogenitorial (that is, both original and pre-filtered) object(s); ii)(one or more) elements within duplicated objects (as in the foregoing)may optionally be scaled or otherwise modified in value; iii) (one ormore) elements within duplicated objects (as in the foregoing) mayoptionally (and, again optionally, repeatedly) execute any combinationof the foregoing operations and, using any or all resultingcomputational products, may augment or enhance (one or more elements) ofthe duplicated data with such additional information within each suchiteration or epoch such that these operations (especially the filteringor augmentation aspects) as described in the various combinatorialmodalities of Assertional Simulation and may be executed in any of thelooped, iterative recursive operations and optimization proceduresdescribed in the foregoing.

Further, any of the computational results produced by combinations ofthe foregoing methods and procedures (and as may exist, for example,within iterations of looped operations, and as may be embodied withinintermediate versions of the subject objects) may also (optionally) beinstantiated as logically and physically discrete and unique (as in theforegoing) and these versions may be preserved and made available toother DTS-based (and DTS-enabled) operations. In variations, suchoperations may be applied repeatedly (optionally creating new (andpossibly indexed and preserved) objects as a result of each iteration)such that any (or all) iteration(s) may be compared to other iterations,other objects and any number of metrics and where in the event that suchrepetitions produce different objects, such objects may be additionallyand optionally be combined, compared and otherwise utilized byevaluative, normative and/or normalizing operations.

The suite of functional capability within composite blocks 1309 and 1406have many practical and varied applications, including but not limitedto the application of non-destructive analytic routines, such as, forexample, situations where versions of Reference Data Models may beprojected forward by applying linear regression algorithms upon certainelements within the data and recalculating and comparing results acrossdifferent iterations. In this context, such results may be fed-back intoprevious stages in the derivation of Reference Data Models, evenintegrating new information as outlined in previous discussions.

Finally, composite blocks 1310 and 1408 (“Index, Scale, Augment andModify Reference Data Sets with Intangible Data 1310”, and “IntangibleData: Attach & Reference Intangible Information to Assertion ModelStructure 1408” in FIGS. 3 and 4, respectively) subsume a collection ofDTS-based (and/or DTS-enabled and/or DTS-accessible) content-sensitiveand content-responsive methods and procedures and interfaces that may becontextually and dynamically assembled (as described in other connectionin this disclosure) by means of which users and groups of users (and, inembodiments, system processes and groups of systems processes as well asdiscrete aggregates of information) may create and manipulatemulti-factor and/or multi-dimensional quantitative (that is, numeric)measures of non-numeric qualities. These measures are called in DTSIntangibles or intangible quantities. Users (and/or processes) maydeploy a variety of interfaces (offered through (and by means of)elements within compound control/data flow Platform-wide Controls andInterfaces 1111) to create, maintain, modify and parameterize theseobjects, providing numeric measures to ineffable or even descriptivequalities. The functionality within composite blocks 1310 and 1408provide but one example of the content-responsive capability describedthroughout this disclosure in connection with many services within aPlatform 1000 but as may be seen in DTS semantic de-referencing as wellas various modalities of Assertional Simulation. DTS Intangibles may beinvoked and parametrized and applied (and even dynamically created) inresponse to the presence of non-arithmetic information (or, invariations, information that may be numeric but upon which a normativeoverlay may be imposed) which must be processed but may also be combinedwith non-numerical qualities (or information inferred or extracted fromsuch information).

DTS Intangibles should be understood in this context in the broadestpossible sense since, in embodiments, they have nearly universalapplication to aggregates of information both within DTS Models and/oras may be attached or referenced to one or more elements within such DTSModels (as may be administered, for example, by one or more elementswithin blocks 1308 and 1405, as described in the foregoing) and whichmay be contextually invoked, created, modified and parametrized. Theseflexible and, in some contexts highly dimensional objects may be basedupon and/or may be applied to and/or may reference (but may not belimited to (any of all) of the following (in any combination): i)qualitative (that is, non-numeric) data, information node(s) and/orpoint(s); ii) subjective evaluation (or opinion) as may be obtained from(one or more) users and entered by means of graphical or other userand/or system interfaces; iii) quantitative (that is numeric) data (asin the foregoing) that is mapped through one or more processes orprocedures. In addition, any of types and variations ofmulti-dimensional DTS Intangibles may also be constructed or invoked orre-parametrized (dynamically, permanently and transiently) based upon(but not limited to) any computational results that may be generated byany or all operations described in this disclosure, including within themodalities of Assertional Simulation and which may be required in thecourse of execution of any aspect and within any computational contextthat may arise in the construction of hybrid-domain DTS Models.Variations of this responsiveness are broadly instantiated within aPlatform 1000 and may include the explicit and implicit results producedby operations performed upon DTS Models, upon forms and variations ofDTS Models, upon ancillary and associated information associated withDTS Models and their various forms as well as results based onoperations executed upon other information that may be internal orexternal to the DTS Platform 1000.

As may be expected, any such information that may be explicitly orimplicitly supplied by application of one or more such DTS Intangiblestructures may be indexed to or otherwise associated with any DTS user,entity or object (as in the foregoing) and may be included within any ofthe other composite blocks within the blocks depicted in FIGS. 1-4 andother figures and descriptions within this disclosure. Moreover, any orall the previously described operations and methods to create andmanipulate DTS Intangibles may be included within any DTS looped,iterative, recursive, and optimization operations As with any suchoperations, the DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces associated with the creation andmanipulation of DTS Intangibles as well as the products of suchoperations operate under RAMS access control and mediation.

The output of these operations, however they may be invoked or executed,as described in the previous descriptions is represented by control/dataflow designated by callouts 1311 and 1049 (“DTS-enhanced Reference DataModels 1311”, and “Assertion Models and Apportionment sub-Modelsaugmented by Ancillary, Associated and Intangible Data 1409” in FIGS. 3and 4, respectively). In FIG. 3, the output control/data flow 1311 ispresented in two places: to the previously-described composite blockcombination of 1117, 1219 and 1220 (in FIG. 2 and other figures; and isadded to the collection of Candidate DTS Models represented by datastores 1206 and 1209 (“Candidate Reference Data Models 1206” and“Cardinal Reference Data Models 1209”, respectively, as depicted in FIG.2 and other figure) and made available to other processes throughout aPlatform 1000.

In many situations, modeling and simulation activities involve sensitiveinformation of an enterprise. This may include information that only canbe accessed or used by certain individuals inside the enterprise or thatcan only be shared under conditions, such as confidentiality agreements,with specific individuals outside the enterprise. For example, the chartof accountants of a business may contain highly sensitive informationthat managers of the enterprise may wish to maintain confidential exceptwith respect to a very small set of individuals. However, certaininformation contained within confidential information like a chart ofaccounts may be very useful for modeling and simulation activities suchas those contemplated throughout this disclosure. Accordingly, a needexists for a system for controlling access to the information used inthe platform.

In many cases, the modeling and simulation activities that can beundertaken using the platform can be quite sensitive. For example, eachof a series of Models that proposes a different apportionment of costsacross the profit centers of an enterprise could, if adopted as adefinitive apportionment, have a significant impact on the personnelwithin the profit centers, such as affecting any profit-basedcompensation that would be awarded to the personnel. A manager ormodeler using the Platform 1000 to explore different apportionmentapproaches may wish to confine access to the alternative Models to avery limited set of individuals. Meanwhile, some other modeling activity(potentially using at least some of the same underlying information) maybe underway, for which an entirely different confidentiality arrangementneeds to be applied. Accordingly, a need exists to provide quitegranular control not only over the underlying reference information usedto create Models, but also over the contents of Models themselves(including Reference Data Models, Assertion Models, Proposal Models,Apportionment models, etc.).

In some cases, access to information handled by the Platform 1000 (suchas in the various Models described throughout this disclosure) may needto be controlled based on the context of the proposed use. For example,in some cases information, such as in an accounting system, about asmall series of transactions might not be very sensitive (because eachis a small part of the overall picture), but cumulative informationabout all such transactions might be much more sensitive (as it mightprovide significant insight about the business). In other cases, (suchas in cases involving regulated personally identifiable information(PII) or sensitive health care information) the opposite may be true.Aggregate information might not be very sensitive, while data relatingto individual transactions might contain highly sensitive information.Accordingly, a need exists for an access control system that iscontext-sensitive.

In embodiments, the access control system may include acontent/context-based, object-based security and access control systemthat can be applied to a variety of multiple-user, content-diversecomputational environments. In embodiments, the access control system isreferred to herein as Recombinant Access Mediation System (RAMS), andreferences to an access control system should be understood to encompassembodiments involving a Recombinant Access Mediation System except wherethe context indicates otherwise.

Access control is a requirement, for example, when using the platformdescribed herein for accounting applications. By providing modelingtools to managers at different levels within a company, those managersmay need to have access to elements of the company's Charts of Accounts(COA), which may be sensitive. More generally, platform users likelywould have limited access to data within a traditional permissionscheme. But in order to actually utilize many features within theplatform described herein, users would (in many likely scenarios)require access, at least certain kinds of access and within certainoperational modes.

As noted above, due to the need to access (and control access to)sensitive information in order to both use and protect the informationof an enterprise, an effective access control system for the platformdescribed may need to be modulated to accommodate the requirement thatusers be able to access (in some fashion) otherwise-forbiddeninformation since that very information may frequently need to beincluded in various platform-enabled calculations. Specifically,platform-enabled capabilities (such as undertaking a budgetaryprojection or creating a “what-if?” scenario) would, in most cases,require a degree of computational access to a company's financial datathat would likely exceed that permitted for most users within anenterprise.

In embodiments, to enable access control, systems may be provided whichinclude elements of object-based, attribute-based, user-centric and/orcentral policy-based designs. For example, some access control featuresmay use elements of a Role-based Access Control (RBAC) approach, such asmodeled on a read-write-view permission scheme (such as used in UNIX)(which in turn may be mapped to role-centric hierarchical structure).However, conventional RBAC may lack the flexibility to handle therequirements of complex simulation and modeling activities. For example,as noted above, many valuable simulations may involve information (suchas information in the chart of accounts) that varies in sensitivitybased on the context of the information, such that merely assigning aset of roles that have permission to access the information is notsufficient to handle the context-dependence. These challenges may bepresent not only with respect to accounting information like the chartof accounts, but to various other heterogeneous data sets, such as thenon-nested, subsumption hierarchical structures described elsewhere inthis disclosure, as well as other data sets used in the data scienceindustry, such as for business analytics. In fact, any system thatprovides sophisticated transformational or re-organizational capabilitymay benefit from an access control fabric that adapts to thattransformation and/or re-organization. The platform described hereinenables various embodiments of extraction of data and transformation ofinformation drawn from various often heterogeneous data sets, where thepermission structure that may have been imposed on the data set(s) priorto Assertional Simulation using the platform may not comport withrealistic post-operational requirements (such as for use of theAssertional Simulation). The prior permission structure may be, forexample, both under-inclusive (such as forbidding access to underlyingdata needed by users to run the simulation) and over-inclusive (such aspermitting access to results of the simulation to those who had accessto the reference data but should not be permitted to see thesimulation).

If an access control fabric is to be secure and effective, it musttypically balance flexibility (with respect to requirements that maychange as the structure of the data changes) with potentially burdensomerequirements for human-mediated supervision. Accordingly, an adaptiveaccess control system may be beneficial for a wide range of data sciencesystems, ETL systems, and the like, including for the platform describedherein. Thus, in embodiments, the RAMS described herein may be used forany data management system wherein data is re-structured but must stillretain access control.

An embodiment of a RAMS is provided below, described in the context ofdepartments of a business; however, it should be understood thatembodiments that involve a wide range of data sets are intended to beencompassed herein, except where the context indicates otherwise.

Referring to FIG. 24, Manager A and Manager B are assumed to beresponsible respectively for parallel Department A and Department B,each reporting to Manager 1 within Department 1 of an enterprise.Department A and Department B are assumed to be subordinate toDepartment 1 in this example. As shown in FIG. 24, Manager 1 hasmultiple departments under management and in most enterprises, wouldtypically have full access to all the accounting information withinthose subordinate departments, while Manager A and Manager B might haveaccess only to accounting information for their respective departments.

An enterprise might have many departments reporting up in this form;however, without limitation, the impact of a RAMS approach may be seenin the respective (and relative) access rights provided to Managers Aand B with respect to accounting information associated with (and as mayapply within) their respective departments.

In an embodiment, Department A and Department B may be modeled as profitcenters A and B within profit center 1 of an enterprise, and theenterprise's chart of accounts may have been mapped (such as usingprojective modeling) such that income and expenses have been allocatedto each department. As noted, one may further assume that it is companypolicy, as typical, that Manager A has full access rights only to allthe chart of accounts items associated with Department A and that,likewise, Manager B has full access rights to only all the chart ofaccounts items associated with Department B. Assume further thatmanagers of other “siblings” departments (ones that report directly toDepartment 1) have the same circumscription of rights to theirdepartments. As noted above, the manager of the “parent” department(Manager 1) is assumed to have full rights to information attached tothe “child” departments. Thus, note that there is at least someinformation attached to Department B that Manager A cannot access, andvice versa. In this example, such information would be called“sensitive” and would carry one or more access restrictions, such asrestricting access to Manager B and Manager 1 for the sensitiveinformation of Department B, etc.

FIG. 25 illustrates the challenges where restrictions imposed by companypolicy are applied in the context of an Assertional Simulation.

In the course of normal activity associated with the platform asdescribed in various embodiments described herein, any of Manager A,Manager B and Manager 1 could be expected to use projective modeling andsimulation, such as to explore the impacts of the allocation of indirectexpenses under their management. These activities can include forwardprojection (as in budgetary proposals), “what-if?”-type backwardprojection (wherein a manager can create results based on proposedchanges to the Assertional Models (or even to Reference Data Modelsets), as well as the application of many visualization, “drill-down”and statistical analytic techniques. As may be expected in a typicalaccounting environment, each “subordinate” or “sibling” profit center(mapped as Departments A and B—as well as those not shown) sharesresources allocated to the parent profit center (Department 1). Thus,for example, Department 1 (the parent) may have $X allocated for the(designated-by-accounting) indirect portion of marketing expenses, andeach subordinate department within Department 1 (that is, the childrenof Department 1) would assume a portion of that cost, perhaps with someportion also be applied to the parent Department 1 in order to accountfor the administrative cost associated with the parent's role of thisaspect of the business. But suppose that the COA item “marketing”(within the indirect expenses tree) has subordinate COA items, some ofwhich are “sensitive” (as defined by company policy). In this context,this means that based on the foregoing, only Manager 1 would have accessto all the information of Department 1, while the respective managers ofthe subordinate departments would have full access to the sensitiveitems assigned to their departments, but not to those assigned to theirsibling departments.

In a typical modeling activity, there may be a Cardinal version of theprofit center cost Allocation Model, one reflecting the “officiallyaccepted” allocation of resources as they are mapped (e.g.,proportionally) to the various profit centers represented by theDepartments. In order to arrive at this Cardinal Outcome Model, theremay have been various Assertional Simulations (such as involvingreapportionment, scaling, and the like) and, to the degree that therewere disagreements, resolution may have been reached. When businesscenters compete for shared resources, there will typically be somedisagreement, negotiation and compromise, and the platform may be usedto model various assertions and Outcome Models as described throughoutthis disclosure. However, as noted above, Managers A and B, who wouldtypically contribute to this activity (and who have a stake in theOutcome Model) are not permitted (in a typically access control system)to have full access to the information they may need, despite the factthat it is stipulated that only SOME of the data is “sensitive” (thatis, only a subset of the data has limited access). This complexity isnot atypical: indirect employee expenses often include confidentialitems, such as benefits and other payments to that employee (assuming,for this example, that the enterprise structures salary as a directexpense and associates other indirect expenses with each employee),whereas some other expenses have no sensitivity and thus could beaccessible to anyone.

Further, in a typical environment, the hierarchy composed within theseitems can be quite deep (e.g., as deep as 6 levels or more) and thus,each node in the hierarchy can have different access rights. Theserestricted nodes can be somewhat randomly distributed throughout thestructure.

Also, note that this data is typically dynamic and may changemonth-to-month and even simulation-to-simulation. Moreover, every timeperiod may be composed of different nodes and different accessrequirements, company policy may stipulate changes to any set of accessrights, and new managers may arrive that may carry different privileges.

Also, apart from changes with respect to a time-evolving CardinalOutcome Model, the platform also provides the capability to createproposed changes to allocations that inherently involve (and may create)sensitive data. Manager 1 may, for example, propose changes toDepartment 1 in a budgetary proposal that could, for example, add newemployees (or move employees from other departments). Details of suchemployee-related indirect costs may be confidential, yet other managerswho may be called upon to evaluate (or propose changes to) this Model.In these cases, the appropriate managers may not, in a simple,department-based access control approach, have the required access tofully evaluate the consequences. Accordingly, an access control systemis needed that balances the need to protect information with the need toenable Assertional Simulation Modeling, such as for cost accounting andother activities. Any ETL scheme, such as for data science, includingbusiness analytics, where data is restructured (regardless of themechanism by which this is done, whether using Assertional SimulationModeling or any other system), will tend to have the same issue, whereat least some of the subject data has access limitations prior toapplication of the ETL processes and the post-transformed data needs tohave at least some access restriction.

In many cases, post-transformation data may be cast within a newontological frame and may be governed by a different vocabulary-definedrelationship structure. Thus, in practical terms, the pre-transformationaccess restrictions may not map effectively to the re-structured data(and in fact do not map in a formal, mathematical sense).

Therefore, any system that needs to preserve the fidelity of accessrestrictions across pre- and post-transformation operations may requirea restatement of these restrictions within the new reference frame. TheRAMS described herein preserves access restrictions acrosstransformation operations (pre- and post-transformation) and providesfor restatement of restrictions within one or more new frames ofreference.

Returning to the example of a projection involving the chart of accountsof an enterprise, among challenges addressed by the RAMS are the need toavoid inappropriate disclosure of accounting data elements, such as viadirect access (wherein a given user must be prevented from directlyaccessing information forbidden to them, where the access may resultfrom intentional, deliberate and rational manipulation by a user oraccidental circumvention of a disclosure expectation such that a usermay, in the course of permitted activity, gain access to which they notentitled) and via inferential access (wherein a given user may be ableto deduce or otherwise determine the nature and value of data forbiddento them based upon information to which they have access). The RAMS maythus address practical hazards (by implementing robust, system-basedsolutions that prohibit accidental and inadvertent circumvention thatmay occur in the course of permitted system activity) and moral hazards(by implementing robust, system-based solutions that address the“clever-user” problem wherein a user may manipulate information accesswithin the constraints of activities permitted to them but such that(with intentionality) the user may determine (or otherwise obtain orinfer) prohibited information). The RAMS may also avoid limitation ofthe capabilities of the platform for modeling and simulation; forexample, the RAMS may help avoid prohibition of access to informationrequired for a simulation where that simulation activity is otherwisepermitted for a user (such that a permitted simulation activity may failto be executed or may execute but may produce incorrect or anomalousresults and/or such that the results of a permitted simulation activitymight not be investigated or “drilled into” as typically desired in useof the platform). The RAMS may also avoid access delimitation thatprecludes proper calculation of results as a result of one or moreelements contributing to the results are inaccessible to a user.

Accordingly, the platform may include an access control system thatgoverns access to information using the platform (such as informationcontained in one or more informational Models, such as Reference DataModels, Assertion Models, Proposal Models, Partitioning models,Apportionment models, etc.). The access control system may include oruse components or sub-systems that provide for authentication of auser's identity (such as password controls, biometric identification,multi-factor authentication, etc.), components of sub-systems for rightsmanagements (such as for data rights management, such as providing forrole-based permissions that govern what users or roles are permitted toaccess what content or information for what purposes), and supportingcomponents and sub-systems to enable the foregoing (such ascryptographic key management systems, including public and private keycryptography systems). In embodiments, the access control system may bea content-adaptive access control system. Thus, a content-adaptive andcontext-adaptive access control system may be provided as part of,integrated with, or associated with a modeling and simulation system,which may include a modeling and simulation system for projection ofportioning and/or apportionment schema upon one or more Reference DataModels, such as to produce one or more Outcome Models for eachprojection. References to an access control system throughout thisdisclosure should be understood to encompass embodiments that involvesuch a content-adaptive and/or context-adaptive access control system,except where the context indicates otherwise. The access control systemmay provide access control to the platform, as well as for one or moreReference Data Models (also referred to in some cases herein as SubjectModels, one or more Proposed Partitioning Models (which may include, forexample Assertion Models and/or Proposal models), one or moreApportioning sub-Models and one or more Outcome Models. As notedelsewhere in this disclosure, an Outcome Model results from thesimulation of Proposed Partitioning Models and/or Apportioningsub-Models upon the Reference Data Models, such that one or more of theApportioning sub-Model and the Outcome Model may be attached to (andoptionally take the structural form of) a Reference Data Model or aProposed Partitioning Model.

The access control system may control access by one or more users, suchas users within an enterprise or users between enterprises, such asusers of an enterprise network, a cloud resource, a hosted environmentfor collaboration, or other environment enabled by informationtechnology components, such as networking and computing componentsdescribed throughout this disclosure.

As described throughout this disclosure, an Assertional Simulation maybe initiated using the platform, which may be populated, parameterized,modified, viewed, accessed, copied, or otherwise acted upon by one ormore permitted users (which may include users specified by individualidentity, by role, by group, or the like). The Assertional Simulationmay use an Assertion Model for modeling the impact of an Assertion Modelfor modeling a proposal. An Assertion Model may optionally include anApportionment sub-Model, which may be subordinate to the AssertionModel. The Assertional Simulation may project the Assertion Model (withany applicable Apportionment sub-Model) across one or more ReferenceData Models to produce one or more Outcome Models. In embodiments, theelements composing each Assertional Simulation are each uniquelyassigned to and associated with a defined user, group, or role, such as,in embodiments, the initiating user or a group or role defined by theinitiating user. In alternative embodiments, the access controls for theelements of an Assertional Simulation may be defined by a manager of anenterprise, by an administrator, or the like.

In embodiments, the access control system may include acontent/context-based, object-based security and access control systemthat can be applied to a variety of multiple-user, content-diversecomputational environments. In embodiments, the access control system isreferred to herein as Recombinant Access Mediation System (RAMS), andreferences to an access control system should be understood to encompassembodiments involving a recombinant access mediation system except wherethe context indicates otherwise.

The RAMS may encompass a novel combination of a number of accesssecurity components to provide content- and context-aware control at agranular level of the elements used in an Assertional Simulation orother activity that involves diverse, multi-party access to, and/orcreation of sensitive data, such as of an enterprise. This may include awide range of activities involved in modeling, simulation, and businessanalytics, including activities of the type disclosed in the use casesdescribed herein.

Referring to FIG. 15, an access control system is provided, which inembodiments may comprise an adaptive, decentralized access-control,privilege administration and execution-regulation system. The accesscontrol system may be applied to multi-user and/or multi-processenvironments. In embodiments, the access control system is theRecombinant Access Mediation System (RAMS), which may consist of acollection of components, processes, procedures, and modules as moreparticularly described herein. The RAMS is a mediation system in thatupon a triggering event it mediates access control and regulatesexecution in situations where computational activities involveconstituent entities that are subject to access control policies. TheRAMS is recombinant in that upon the triggering event it may synthesizea policy using the various access control policies that apply to theconstituent entities that are involved in a given computational activitythat is governed by the RAMS. More particularly, the RAMS isevent-triggered and is initiated by one or more computational events,each referred to herein as a “RAMS-eligible computational event.”RAMS-eligible constituent entities can initiate, contribute to andparticipate in one or more RAMS-eligible computational events. EachRAMS-eligible constituent entity may be associated with (such as bybeing indexed or bound to) an access policy, referred to as applied to agiven constituent or entity as a “local access policy” for thatconstituent entity. The local access policies may be stored in a datastore, such as being subsumed within a more general information storerelating to the constituent entities, referred to herein as theentity-associated ancillary data 10101 store. Upon initiation of aRAMS-eligible event, RAMS-eligible constituent entities submit (andoptionally may mutually exchange) their respective local access policiesto the RAMS, which in embodiments may occur by reference to theentity-associated ancillary data 10101 store within which the applicablelocal access policy is stored. Upon of a RAMS-based event, one or moreRAMS procedures (among various methods and procedures that may beassociated with the control fabric of the RAMS) may generate and storeone or more event access control policies. Each event access controlpolicy may be synthesized by the RAMS for (and may be indexed to) theparticular event that triggered the use of the RAMS and for theparticular entities involved in the event. The event access controlpolicy is synthesized by the RAMS based on the access privilegesassigned to each of the RAMS-eligible constituent entities, e.g., byrecombining the access privileges assigned to each of them as reflectedin the local access policies stored in the entity-associated ancillarydata 10101 store (or pulled from or supplied by any other source of suchlocal access policies). Thus, the event-triggered policy synthesisprocess (created “recombinationally”) may be understood as aRAMS-executed “mediation” or reconciliation of the collective accessinformation submitted by or applicable to RAMS-eligible constituententities. It may be noted that in some instances, a synthesized eventpolicy may be re-applied to an identical event, such as where all theparticipating elements are identical, and all participating elementsretain identical access information. In these cases, the synthesizedevent policy may be saved (and re-used) in subsequent RAMS-controlledcomputations. Such re-use may help optimize the RAMS operations (such asby bypassing repetitive operations). Saving event access controlpolicies may also be used to create logs and “audit trail” information.

FIG. 15 illustrates a general expression of the “entity-centric”modeling of the RAMS architecture, where constituent entity typesinclude users, system processes and data elements that may have localaccess control policies and may be involved in computational processes,access requirements, or the like that benefit from event access controlpolicies like those created using the RAMS. Entities may include, inembodiments, users (including, as applicable, user groups) 10002, systemprocesses 10003, and data elements 10004.

A “user” may represent one or more operators or agents (or, invariations, automated or semi-automated proxies which may act on behalfsuch operators or agents, where any such “user” may, in some contextsand applications, comprise combinations of the foregoing) that mayassert control and/or may interact with a system that uses the RAMS(e.g., where access control for some aspect of the system is under RAMScontrol). These may include individuals or groups of humans, enterprisesand other legal entities, and semi-automated or fully automated agents,such as computational agents. Each entity may represent a distinctoperational construct operating in the context of the RAMS (e.g.,interacting with or within a control fabric of the RAMS), such asthrough one or more interfaces.

FIG. 16 illustrates the association of ancillary data with entities,where each type of entity “possesses” or “owns” associated, locallyrelevant entity-associated ancillary data 10101, as denoted by elements10105, 10106 and 10107, respectively. Thus, users 10002 own user (anduser groups) objects 10102, system processes 10003 own system processobjects 10103 and data elements 10004 own data elements objects 10104.

Note that the RAMS-based entity-associated ancillary data 10101 refersto “local” data associated for each of the entities, where “local” maybe defined within RAMS in terms of computational context (e.g.,associated with a local computational capability or storage system), interms of informational content (e.g., the content is local to aparticular user or group of users, such as having been created by thatuser or group), and the like. In general, locality of the data reflectsand composes information germane to the logical (and in some cases,physical) disposition of the referenced entity, where the informationcomposed within the entity-associated ancillary data 10101 (and/orwithin the applicable local access control policy) may include, withoutlimitation, the identity of the associated entity (such as, for example,one or more cryptographic signatures, passwords, or other uniqueidentifier(s)), where such information may include one or more tokens(as in the foregoing) which may serve to validate or otherwise verifythe identity and/or disposition of the associated entity.

The data structures and information composed within theentity-associated ancillary data 10101 may also include one or moreblocks which may (directly or indirectly) represent or encode (and/oroptionally encrypt or otherwise obfuscate) privileges, such ascomputational privileges, exchange privileges, access privileges,associational rights or licenses, and the like, as well as othercomputational dispositions. Such information relative to access andprivileges may compose the local access policy germane to that entity.Such local access policy informational blocks may be accessible via abi-directional exchange interface as may be applied to and from otherdiscrete entities. In other cases, the exchange may be multi-lateraland/or multi-directional, involving many entities (as may be seen inbroadcast or publish-subscribe architectures). In the context of aRAMS-triggered event, one or more entities may attempt to associatewith, use or otherwise process the information encompassed by and/orreferenced by the associated entity.

The information composed within the entity-associated ancillary data10101 may further include one or more discrete conditional and/orlogical formulations wherein local or system-based (orsystem-accessible) or user-initiated conditions may be specified, on thebasis of which information may be, for example, accessed, indexed orotherwise associated with a discrete entity. While often primarilyinformational, such entity-associated objects may also contain or mayreference executable logic, conditional procedures and other constructsthat can be processed for most than merely informational purposes.

The information composed within the entity-associated ancillary data10101 may also include one or more informational blocks (or referencesto other informational blocks) that may represent or encode pastinteractions, such as may have been executed within a previouslyexecuted RAMS-based event. Such a history may represent or encode, forexample, the disposition of or with respect to the associated entitywithin such events.

While in certain examples provided herein regarding a RAMS system, theentity-associated ancillary data 10101 structures are informationalonly, entity-associated ancillary data 10101 structures that includeexecutable logic, conditional procedures, or other operational elementsare intended to be encompassed, except where the context indicatesotherwise.

FIG. 17 illustrates an embodiment of a data architecture for a RAMSsystem involving entity-associated ancillary data 10101 structures, suchas may be deployed in a computational system that enables a community ofusers, having a suite of system-based (or system-enabled) procedures andprocesses and system-resident (and system accessible) data elements. Inthis example, users, system processes and data elements are eachrespectively associated with indexed entity objects. Various users 10002may be associated with indexed user objects 10201 in a process 10205.Various system processes 10003 may be associated with indexed systemprocess objects 10203 in a process 10203. Various data elements 10004may be associated with data element objects 10202 in a process 10206.

However, entities and entity-associated ancillary data 10101 structuresneed not always be handled as separate, discrete elements. For example,in practice (and in some implementations) ancillary data structures maybe integrated with or subsumed within the subject data structure for anentity; however, such integration or subsumption does not diminish thediscrete and separable nature of that information in the context of thefunctionality provided by the control fabric of the RAMS. Thus, in thedisclosure herein relating to RAMS, the entity-associated ancillary data10101 structure, whether or not explicitly shown, is assumed to bepresent and logically (and in some cases, physically) distinct anddiscrete.

An embodiment of an entity-associated ancillary data 10101 structureused in a RAMS system may comprise a certificate. Such a RAMScertificate may comprise a standalone entity-associated ancillary data10101 structure with which the RAMS system may operate or may optionallycomprise an element within an informational store that has a broaderpurpose than the RAMS system. Thus, except where the context indicatesotherwise, in this disclosure a RAMS “certificate,” an“entity-associated ancillary data 10101” and the “local access policy”embodied in a certificate or entity-associated ancillary data 10101structure may be understood to serve the same or similar purposes, andreferences to one of them should be understood to optionally encompasssimilar embodiments that use any of them.

In embodiments, the information exchange surrounding a RAMS-based eventmay utilize subsets (e.g., “tokens”) of an entity-associated ancillarydata 10101 structure, such as a RAMS certificate. RAMS certificates andtokens may be uniquely indexed to or referenced by one or more entities.In some embodiments, RAMS operations may include both certificates andtokens, such as where a RAMS certificate comprises a superset of theinformation typically composed within a RAMS token. In embodiments, aRAMS certificate may contain more than one token. A RAMS token may insome cases contain less control information than a certificate, but atoken may typically compose a RAMS-valid identifier of its associatedentity.

RAMS certificates or tokens may be associated with or indexed toRAMS-eligible constituent entities in numerous (typically non-exclusive)arrangements, including without limitation any or all of the following(as well as in combinations of the following). In embodiments, acentral, global repository (such as a registry) may be providedcomposing a system-wide information store accessible to RAMS-eligibleconstituent entities. In such embodiments, one or more certificates ortokens may be composed within this central store and indexed to (orotherwise associated with) one or more RAMS-eligible constituententities. In some cases, such repositories may be logically and/orphysically arranged by a type of RAMS-eligible constituent. In otherembodiments, a partitioned global repository or registry may be providedreflecting a sectioning of an otherwise system-wide global store (as inthe foregoing example), such that the partitions integrate a limitedaccess scheme for RAMS-eligible constituents based upon one or morepartitioning criteria, such as system partitioning, user partitioning,and the like. In embodiments, a peer-based, decentralized distributionof certificates or tokens may be provided, wherein RAMS-eligibleconstituent entities integrate one or more RAMS certificates or tokenswithin or associate one or more RAMS certificates or tokens with thelogical structure of the entity. A RAMS overlay may integrate more thanone of the foregoing configurations, such that, by its recombinatorialnature, RAMS permits dynamic re-organization of these configurations(both transiently and permanently) depending on computational, contentand other system- and user-based contexts, as well as based on input andother conditions.

RAMS certificates or tokens (as may be distributed as noted above) maybe exchanged by means of numerous (non-exclusive) techniques, includingwithout limitation any one of or any combination of the following. Inembodiments, centralized or global certificate and/or token managementmay be provided, wherein the RAMS control fabric is managed by acentralized set of methods of procedures within or accessible to thesystem. In embodiments, partitioned certificate or token management maybe provided, where a global or centralized system is partitioned. Inembodiments, peer-to-peer (including intra-constituent andinter-constituent) direct exchange of certificates may be enabled. Inembodiments, a RAMS overlay may integrate more than one of the foregoingexchange techniques, such that, by its recombinatorial nature, the RAMSpermits dynamic redefinition of these deployment configurations (bothtransiently and permanently) depending, as in the foregoing, oncomputational, content and other system- and user-based contexts, inputand other conditions.

RAMS certificates or tokens may optionally integrate one or morecryptographic and/or encoding techniques wherein RAMS certificates (ortokens) may integrate (or otherwise possess or may be indexed to) one ormore unique cryptographic keys, signature(s) and/or otherauthentication-related elements. Such techniques may be used for anynumber of reasons including without limitation verification and/orvalidation of the identity of the RAMS-eligible entity, verificationand/or validation of the information within the certificate or token,and/or verification and/or validation of the computational contextwithin which the RAMS-eligible event may be executed. A typicalRAMS-integrated validation scheme may include one or more methods andprocedures. For example, a validation scheme may generate multi-partyhomomorphic keys, homomorphic signatures and other similarly configuredmulti-party authentication-related encodings, distribute such keys andsignatures and encodings, and validate such keys and signatures andencodings, thereby validating the associated information. Such methodsand procedures may be organized in any number of configurations as inthe foregoing, ranging from global-based to partition-based topeer-based process implementation, or combinations of them. Suchvalidation techniques may be dynamically and conditionally invoked,bypassed or modified based on upon any number of conditions, includingbut not limited to the system context, specific content within orrelated to a RAMS-eligible event and/or one or more qualities containedwithin, composed within, referenced by, related to, and/or associatedwith one or more of the participating entities. Such conditionalinvocation criteria may be system-based and/or user-invoked and may beapplied at the system-wide or applied locally to one or moresub-sections within a system.

The RAMS may be seen as at least partially entity-based in that, asdescribed in the foregoing, certificates, tokens and associated localaccess policies may be bound to an entity (or group of entities). Asdescribed, RAMS-eligible entities include but are not limited to any orall of the following (or logical representations of the same): systemusers and/or groups of users, system-based (or system-accessible)processes and procedures and/or groups of such processes and procedures,and data elements and/or groups of data elements, as well as variouscombinations of the same that may be composed within or accessible tothe system in which the RAMS is used or with which the RAMS isintegrated or associated. Variations of RAMS implementations may furtherinclude “hybrid entities” that may combine one or more propertiesassociated with any of the foregoing entities. Such “hybrid entities”may, for example, include devices and/or groups of devices. Such devicesand/or groups of devices may, in some cases, be contextualized as“entities” within the system, thus possessing one or more of thecharacteristics of the foregoing entities. For example, a user'sdedicated device may in some cases be treated as the user for purposesof the RAMS. In embodiments, certificates, tokens and associated localaccess policies may be permanently or transiently bound to one or moresubject entities based upon various factors, such as computationalcontext, event-related criteria, and/or a specific (e.g.,ontologically-defined) content-based characteristic within or associatedwith the subject entity (such as a meaning that may itself be fungible).Records or binding history may be maintained and referenced and/orreused for various purposes.

Based upon this entity-centric nature, the RAMS control fabric may beoptionally and dynamically applied and/or extended throughout a systemor part therefore to manage access, interaction and association to, byand among users, data elements, and system processes.

RAMS-based recombinatorial mediation may modulate and control(optionally and contextually) on a per-element and selective basispermission-based functionality encompassing, but not limited to, any orall of the following. In embodiments, RAMS mediation may limit or permitaccess to and between constituent entities. In embodiments, RAMSmediation may conditionally attenuate or extend access to, by and amongconstituents. In embodiments, RAMS mediation may also add, elide, editor otherwise modify policy-based (or policy-derived) logic as may applyin the context of a subject event and with respect to any subjectentities. Such access-related adaptations or mediations may be appliedto any or all of a wide range of inter-entity and intra-entityarrangements. These may include execution of one or more computationaloperations utilizing discrete data elements or groups of discrete dataelements (including other informational stores indexed to but logically(or physically) independent such elements), where such data elements mayhave one or more independent RAMS indices and where each computationaloperation may also have one or more independent RAMS indices.Arrangements to which RAMS may be applied also may include computationalor associational indexing between discrete data elements (and groups ofdiscrete data elements). Embodiments may include utilization of and/oraccess to discrete data elements (and groups of discrete data elements)as in the foregoing within and by one or more discrete system-based orsystem-accessible computational methods and procedures (or groups ofsuch by system-based or system-accessible computational methods andprocedures), where such discrete procedures may have (one or more)independent RAMS indices. Embodiments may include utilization of and/oraccess to discrete data elements (and groups of discrete data elements)as in the foregoing by system users (or groups of system users), wheresystem users (and groups of users) may have (one or more) independentRAMS indices. Embodiments may include utilization of and/or access bysystem-based or system-accessible discrete computational methods andprocedures as in the foregoing by system users (or groups of systemusers) as defined in the foregoing.

While RAMS is entity-centric, its architecture is further characterizedby its event-driven functionality. An event handled by the RAMS providesa computational pivot point around which complex multi-factor permissionmediation may be executed. This may occur in a wide variety ofenvironments and conditions.

Referring to FIG. 18, a RAMS event sequence 10305 is illustrated. At astep 10301 RAMS-eligible entities may (directly or indirectly) cause, ormay cause to be triggered, a RAMS event, resulting in a RAMS eventtrigger 10302. A RAMS event should be understood as any computationevent executed within the context of, in association with or as may beindexed by a RAMS control fabric. This may include almost any activitywithin a computational environment in which information may beexchanged, processed, presented, modified or utilized.

In embodiments, the event trigger results in the creation of an eventcertificate at a step 10303. This may lead to the execution of an eventat a step 10304. The event certificate may comprise a pre-requisite forthe execution of a RAMS-eligible event; however, exceptions may apply tothis requirement in some cases as noted elsewhere in this disclosure.

FIG. 19 provides one example (among many possible examples) of anevent-based sequence 10305 that is characteristic of the occurrence ofan event in the RAMS. One or more RAMS-eligible entity types 10001 maytrigger an event in a process 10301, involving assembly at a step 10401of participating entity certificates 10402 or tokens (such as usercertificates 10403, process certificates 10404 and data elementcertificates 10405). The RAMS system may create, at a step 10303, a RAMSevent certificate 10406 based on the various participating entitycertificates and other factors as noted above. Then the RAMS system mayexecute the event at a step 10304.

In embodiments, RAMS event sequences may be initiated by a process orsystem external to the RAMS control fabric, such as through anexternal-facing API or other interface, such that a RAMS-based eventmay, in response, be executed within the purview of the applicable RAMScontrol fabric. Such an external process may also in some cases becovered under a different RAMS control fabric. In some such cases, twoor more such control schemes may co-exist within a host system.

Referring still to FIG. 19, one or more RAMS-based methods andprocedures assemble the certificates associated with the participatingentities (as detailed in the foregoing). The assembled certificates (orelements therein) are passed to a generalized process, such as, in thisexample illustration, the “Create Event Certificate” process 10303. Asnoted in the foregoing, such certificates or tokens may form a part of alarger information store associated with those entities. The certificateor token (or the larger information store) may comprise the “localaccess policy” for that entity. In this manner, the RAMS event sequencemediates RAMS-validated computational events. Such events are generallyconditionally executed upon dynamic synthesis of event certificates (asdescribed in the foregoing) according to the privileges and constraintsembodied within the contributed certificates. This event certificateprovides enablement of authentication for execution of the subject eventwith the participation of the associated entity or entities. Suchenablement may proceed within various scenarios, including but notlimited to, single computational events; one or more series of connectedor correlated computational events; and/or one or more collection ofdiscrete or distributed (and nominally unrelated) computational events(events, that is, which may be unconnected within the currentontological context except as bound by the certificate-basedenablement). Any or all such scenarios (as described elsewhere in thisdisclosure) may, in some cases, be contained within (or referenced by orproduced as a result of) one or more looped (or conditionallyrepetitive) sequences, and/or one or more recursive and/orself-modifying operations.

RAMS event certificates may generally (but not necessarily always)contain a “certificate identifier,” which may be, for example,cryptographically valid or otherwise secure signature. The certificateidentifier may be unique to the event and the entities designated forparticipation in the event.

FIG. 20 provides a different view of the event sequence, where theresult of the computation resulting from the event sequence is referredto as a RAMS instance 10505. In this simple illustration of the conceptof RAMS-based event triggering, RAMS-eligible user and data objects, aswell as data elements and external APIs, are termed computationalconstituents 10501. The computational constituents may be involved inthe creation of certificates or tokens at a step 10502, described inmore detail elsewhere in this disclosure. At the start of a RAMS event10503, upon validation of the “credentials” (certificates or tokens)associated with these computational constituents 10501, one or moreprocesses produces a computational result. This sequence, referred to asan instance computational chain 10504, produces, as the RAMS event iscompleted 10504, a RAMS instance 10505. The instance computational chain10504 may include the creation of an event certificate 10303, afterwhich one or more system processes 10003 may produce system processcertificates 10404 that may be included in or associated with the eventcertificates for the event. Once this has occurred, the event may beexecuted at a step 10304 and the RAMS instance 10505 created.

RAMS event sequences embody the recombinant aspect of RAMS in that thecreation of the RAMS event certificate is made possible by combining theelements contributed by each RAMS-eligible participating entity.Further, the RAMS event certificate embodies the mediation aspect ofRAMS in that each RAMS-eligible entity participating in the creation ofan instance is granted a set of rights, privileges and dispositionallogic unique for that entity in relation to (and valid for) thatcomputational event. These event-bound rights, privileges anddispositional logic constructs may be composed within the eventcertificate and indexed to or otherwise associated with the subjectentity. Thus, the RAMS control fabric is both entity-based andevent-driven, where a central operational feature is the synthesis of anevent-bound and event-germane control object (the event certificate)that is based upon the disparate access and privilege informationprovided by each RAMS-eligible entity.

As outlined in the foregoing, RAMS-eligible entities are fundamental tothe operation of the recombinatorial mediation processes. In order toaddress the demands of a variety of conditions in variousimplementations these entities and associated ancillary entityinformation may be configured in a variety of ways, such that they maypossess, be indexed to, or otherwise associated with, a variety ofinformation stores (the entity-associated ancillary data 10101 notedabove).

FIG. 21 provides a generalized overview of the user entity andillustrates one possible configuration among many possibleconfigurations. In this illustration, a system user (the term includingusers and user groups 10002) possesses, is assigned and/or is indexed toor otherwise associated with a user object 10102 (including user anduser groups objects). In this example, the entity-associated ancillarydata 10101 is referred to as user-associated ancillary data. TheRAMS-relevant aspect composed within this user-associated ancillary data(referenced by callout User-Associated Ancillary Data 10601) is referredto as a local access policy 10602, which comports with descriptionsoutlined in the foregoing. Other information 10603 may be attached tothis user-associated ancillary data object, including but not limited toboth RAMS-related data and data unrelated to the RAMS information butotherwise germane to the user. The RAMS-related information within thelocal access policy is considered “local” in that it applies to theparticular user as applied in the context of RAMS-based event. Thisassociation is illustrated in this example by callout User PossessesLocally Relevant Data 10604. The local access policy may include withoutlimitation the information required to execute the subject user'sdisposition with respect to any RAMS-based events within the mediationprovided by RAMS.

FIG. 22 illustrates the manner in which a RAMS system may operate withina community of users 10701 (sometimes collectively referred to herein asa “user pool” or an eligible user pool 10704). Each RAMS-eligible user10702-A through 10702-B where eligible user pool 10704 depicted in FIG.22 as composed of User 1 through User N and where each such userpossesses a unique, corresponding (in this example) user object 10703-A(User 1) and 10703-B (User N). This is one example among many possibleexamples of a context within which RAMS-eligible users may interact withother users and other processes (such as decentralized and distributedstatus and control information) in the presence of the RAMS controlfabric. By an indexing process 10705, ancillary data 10706-A (for User1) and ancillary data 10706-B (User N) may be possessed by the users inthe eligible pool 10704, as noted by callout 10707 where, in thisexample, ancillary data 10706-A and ancillary data 10706-B are composedas part of user objects 10703-A (User 1) and 10703-B (User N). Thisancillary data (10706-A (User 1) and ancillary data 10706-B (User N)comprising of at least two primary structures: user access policies10708-A and other optionally included user info 10709-A (User 1) anduser access policies 10708-B and other optionally included user info10709-B (User N). These may be used to provide user-specific anduser-unique addressable control and status data as noted by callout10710 (User-specific & user-unique addressable control and status data).The ancillary data 10706-A and 10706-B, including user access policiesfor Users 1 and User N 10708-A and user access policies 10708-B,respectively, and other user information for Users 1 and User 10709-Aand 10709-B respectively may optionally be used to provide decentralizedand distributed status and control information 10711.

FIG. 23 shows one example (among many possible examples) where one ormore system processes (which may be internal or external systemprocesses and which may use internal or external resources with respectto the system to which the RAMS is applied) may each be characterized byone or more unique system process objects, 10802-A and 10802-B (shown inthis example as associated with system process 1 and system process M,respectively), noting that individual system processes and systemprocess groups that may be grouped into process eligible poolsreferenced by callout 10803 (DTS-based & DTS-enabled processes may begrouped into process-eligible pools), with each process or groupincluding a unique identifier and which are shown in this example ascomposed of system process 1 (10801-A) through system process group M(10801-B). Note, also that this example shows both an individual systemprocess (system process 1 10801-A) where in this instance (among manypossible variations), system process 1 10801-A represents an instance ofa process whereas system process group M (10801-B) represents acollection of processes. Callout 10812 “Example System Group AncillaryData” highlights that the representation in FIG. 23 depicting such poolsand groupings (referenced in the foregoing and depicted by callout10803) is merely one of many possible arrangements. Each process andprocess object may be indexed or associated with entity-associatedancillary data 10101 in one or more steps 10804 (Indexing to Processesand Process Groups) where this data is composed, in this example, within10805-A, 10805-B, where the collective results are referred to in thisexample as process associated ancillary data 10809. Each process maythus “possess,” or be associated with ancillary data structures asreferenced by callout 10806 (All Processes or Process Groups “Possess”these Structures), locally relevant data and may be subject to one ormore local process access policies 10807-A and 10807-B (referenced tosystem process and/or process groups 1-through-M, respectively), suchthat the execution of the process may be mediated by the RAMS, such asby conditioning execution of the process upon one or more conditionsexpressed in an applicable local access policy. Each process may alsoinclude other attached information 10808-A and 10808-B (again referencedto system process groups 1-through-M, respectively). As with users,process and process-group specific and/or unique, addressable controland status data 10810 may be enabled based on the process-associatedancillary data 10809. Process and process-group-specific status andcontrol information may be decentralized and distributed as indicated bycallout 10811.

System processes and system-enabled processes (and groups of suchprocesses) each have associated process objects 10802-A and 10802-B andhave per-process (and per process group) ancillary data 10805-A and10805-B, including local access policy information 10807-A and 10807-Bfor the process and other attached information 10808-A and 10808-B,collectively comprising process-associated ancillary data 10809. Eachsystem process and each system-enabled process can possess local accesspolicies and other data relevant to the system process or system-enabledprocess, respectively. Data for a system process or a system-enabledprocess may be supplied to the RAMS or pulled by the RAMS throughvarious sources, such as from one or more distributed data sources (suchas located within the computing environment of an enterprise or locatedin the cloud), through use of one or more APIs or other interfaces toone or more external systems, or the like. The RAMS system may handlemany such systems processes and systems-enabled processes with theirrespective objects and process-associated data (including local accesspolicies).

Referring to FIG. 24, in embodiments, system processes may also include“native” or internal data elements (such as data elements occurringwithin the system to which the RAMS is applied) and external dataelements (such as data elements in a system to which the RAMS isapplied, or accessible to the system to which the RAMS is applied, orthe like).

Referring to FIG. 25, example data element 10101 depicts data element10901-A (referenced to example data element P-c) and including dataobjects associated with data element P-c 10902-A. The data objectscomposing 10902-A are depicted in this example representation asassociated with ancillary data for data element P-c 10903. In thisexample, ancillary data 10903 may, in turn, contain (and/or mayreference and/or may have logical access to) a collection of dataelements grouped (in the FIG. 25 example depiction) within (or logicallyassociated with) associated data for data elements grouped as cited bycallout 10904. In implementations, this section of 10903 may includemultiple informational elements related to, in this example, dataelement P-c 10901-A via 10902-A. Such a constituent is shown in thisfigure as an attached local access policy represented by data elementP-c local access policy 10905-A. In addition, as shown, 10904 may, invariations, also include additional information represented in dataelement P-c other attached info 10906-A. The depicted variation of10905-A and 10906-A is further referenced by two-part callout 10907(data elements & groups of elements “possess” addressable “local” accesspolicies, and data elements & groups of elements “possess” addressable“local” “other info”, respectively). Callout 10909 (data elements &groups of elements “possess” “locally” relevant information) furtherelucidates this variation of the informatic structure revealed by 10904(and its example sub-elements 10905-A and 10906-A). Further, as noted bycallout 10908 (element- & element group-specific status and control infois decentralized and distributed), the objects and ancillary dataassociated with data element P-c 10901-A (10902-A, 10903, 10905-A and10906-A) as well as Other Data Elements & Groups of Elements 10910 mayin variations be decentralized, distributed. Callouts 10910 (other dataelements & groups of elements) and 10911 (additional internal dataelements- & element group-specific ancillary data) convey in FIG. 25that, in implementations, the example arrangement of affiliated objectsdescribed in the foregoing may include or be associated with elements10910 and 10911 via 10910. In addition to “internal” data elements(e.g., 10911), external data may be accessible through use of one ormore APIs or other interfaces to one or more external systems 10913.Element 10915 (example external system-enabled data element) mayindicate an exemplary system-enabled external data element as depictedin 10901-B (data element Py-z) and, in this variation among many otherpossible variations, reflects similar associated data objects (10902-B)as previous outlined in the foregoing for 10901-A and 10902-A. Likewise,10914 (associated data for external data elements) describes 10905-B(data element Py-z local access policy) and 10906-B (data element Py-zother attached info) may be similar in structure and relationship tolocal access policies and other attached data for 10904 Associated datafor Data Element. Likewise, callouts 10916 and 10917 (external dataelements & groups of elements “possess” addressable “local” accesspolicies, and external data elements & groups of elements “possess”addressable “local” “other info”, respectively) detail some propertiesof 10905-B and 10906-B respectively. Callout 10912 (externalsystem-enable data elements & groups of data elements) is bracketed withan indicator pointing above to callout 10910 (and therefore to any orall of the elements associated with 10910 in the exemplary embodiment ofFIG. 25) to show that external system-enabled data (exemplified byelements 10915, 10901-B, 10902-B, 10905-B, 10906-B, 10916 and 10917)may, in variations, be treated as logically (or virtually) contiguoustherewith. Further External system enabled data (e.g., 10912) mayreference, through use of one or more APIs or other interfaces, one ormore external systems 10913, or the like.

Referring to FIG. 26, RAMS entities of any type may be (transiently orpermanently) clustered or grouped or aggregated, optionally withoutlimitation, but optionally as modulated or limited by one or moreconstraints mandated by or through the system, such as by a host oroperator of the system. In the absence of constraints, such aggregationsmay also in one or more obverse operations be de-clustered or ungroupedor disaggregated. RAMS entities may be aggregated (or disaggregated) asmay be required by or as may be desirable for any reason in theoperational context of a given system and may result from, withoutlimitation, various groupings. Embodiments may include system-mandatedgroupings, wherein groupings or aggregations may be initiated by ordissolved by or otherwise modified under system-based command, such asmay be administered by an appropriate user (or agent) through one ormore interfaces. Embodiments may include user-mandated groupings, suchas wherein groupings or aggregations may be initiated by or dissolved byor otherwise modified under user command through one or more userinterfaces. Embodiments may include context-based groupings, such aswherein groupings or aggregations initiated or required by one or morecomputational elements. Embodiments may include content-based groupings,such as wherein groupings or aggregations are initiated or required byone or more aspects composed within or associated with an entity.Embodiments may include installation-mandated groupings, such as whereinentity groupings may be required by or may be desirable for any reasonin the operational content of an instance of a given system. Embodimentsmay include context-derived groupings, such as wherein entity groupingsmay be required by or may be desirable for any reason based upon acomputational or operational condition particular to an instance of thesystem but which may not be present in all systems. Embodiments mayinclude content-based groupings, such as wherein entity groupings may berequired by or may be desirable for any reason based upon the contentwithin one or more entities. Embodiments may include history-basedgroupings, such as wherein entity groupings may be required or may bedesirable based upon one or prior events or occurrences or mandatesrelated to any or all of the foregoing. Embodiments may include externalinput or mandates via one or more interfaces or API's, such as wheresuch external requirements may be based upon any combination of theforegoing, within the subject external system or otherwise accessible toit.

RAM entity groupings may be spawned transiently or permanently, but inany case, such RAM entity groupings may be preserved and indexed andthereby made addressable within the system and thus may be seen asaccessible within or by the host system. Such addressability does notrequire accessibility, merely that a grouping may have a logicalidentity if composed.

The boundaries animating RAMS entity groupings may be defined by one ormore conditions embodied within an informational entity referred to hereas an “entity-association rule.” An entity-association rule may bedynamically created, such that the criteria may be generated within orin response to a computational or content-derived context (and/or inresponse to factors linked to or otherwise indexed to such factors). Anentity-association rule may be statically created, such that thecriteria may compose or may be derived from one or more rules (orrule-sets) which govern the grouping of entities irrespective of dynamicconsiderations. An entity-association rule may be statically created butdynamically modified, such that one or more entity-association rules,which may have instantiated as static, may be dynamically modified orotherwise changed based upon the foregoing, where such modifications maybe transient or permanent, such that the criteria as may be embodiedwithin an entity-association rule.

FIG. 26 shows an example among many possible examples of animplementation of entity grouping of user entity aggregation. Theentity-associated ancillary data 10101 structures noted above are notshown but present. User aggregation may be managed by various processes,including automatic (e.g., programmed) processes, human-managedprocesses (such as enabled by one or more interfaces of the RAMSsystem), human-assisted processes, and/or inductive and/or data-drivenprocesses. Aggregation may be implemented by one or more interfaces,such as application programming interfaces.

The element manage user aggregation refers to the net functionalitywhereby a subset of users may be selected for inclusion within one ormore groupings. This illustration conveys example functionalityrevealing selection of users to create an aggregation (or selection ofadditional users to augment an existing aggregation), similarcapabilities can be used for de-selection and/or disaggregation. Amongother things, user aggregation allows a RAMS system to handle groups ofusers and appropriate user access policies (such as ones that apply atthe group level).

Referring to FIG. 27, system processes and system-enabled processes,such as those described above may also be grouped or aggregated, usingsimilar capabilities and mechanisms described for user aggregation inconnection with FIG. 26. This may occur under governance of one or moreprocess aggregation rules. Similar capabilities may be used todisaggregate or un-group processes from one another. System processesmay thus be handled as groups, such as where a given local access policygoverns an entire group of system processes. In an exampleimplementation in FIG. 27 (one of many possible variations of thesemethods), functional block 10952 (manage DTS Dynamic Data Aggregation)is shown as receiving input from many possible sources and of manypossible types denoted by callout 10951 (all DTS-based & DTS-enableddata) which may include: a) 5102 (heterogeneous data elements); b) 1172A(pre-DTS internal & external Data—data not yet processed in any DTSprocess, as described in the foregoing); c) 1172B (Post-DTS Internal andExternal Data 1172B—data previously processed in a DTS process, asdescribed in the foregoing), d) 13001 (Assertion Model Collections, asdescribed in the foregoing); and e) 12001 (collections of Reference DataModels, as described in the foregoing). Functional block 10952 providesits output to composite block 10956 RAMS Association Rules denoted inthis example representation as consisting of data stores 10955D (UserAssociation Rules), 10955C (Process Association Rules), 10955A (Instanceand other association rules), and 10955B (Data element associationrules). Note, however that the following functional blocks and dataelements also contribute to the compositions within 10956, including(but not in all variations limited to): a) functional block 10953A(Generate certificates for association rules for data elements); b)10905-A (data element P-c local access policy—an example detailed inreference to FIG. 25 and in other descriptions in this disclosure); c)10953C (data element P-c association rules); and d) 10906-A (dataelement P-c other attached info—also described, among other places,throughout this disclosure). These components comprising the data storeswithin 10956 are referenced for convenience as a compound data signal10955 (RAMS association data). Note also that 10101 (example dataelement P-c) composed of 10901-A and 10902-A, as described in theforegoing represent the associated data element and ancillary dataobjects from which 10905-A, 10953C and 10906-A may, in variations bereferenced or reposed. Note, as well that 10905-A is shown in thisexample as optionally inserted, a variation denoted by callout 10953D.Callout 10952B (Generate data element P-c association certificate)details a variation wherein 10905-A passes information to functionalblock 10953A (as does 10952) which, in implementations, may composemethods and procedures that produce association rules for data elements.In variations, 10953A may also provide one or more certificates toexample data element 10953C as depicted by callout 10953B. Further,composite data signal 10955 is depicted as possessing bi-directionalinput/output functionality to convey variations in which the operationsdescribed in FIG. 27 as optionally executing bidirectionally). Callouts5009, 5008, 1008A and 1109 denote that these operations may, invariations, be associated with these select processes (5009), mechanisms(5008) and controls (1108A and 1109) within Platform 1000.

Referring to FIG. 28, data elements, such as those described above mayalso be grouped or aggregated, using similar capabilities and mechanismsdescribed for user aggregation in connection with FIG. 26 or systemprocess aggregation in connection with FIG. 27. This may occur undergovernance of one or more data element association or aggregation rules.Similar capabilities may be used to disaggregate or un-group dataelements from one another. Data elements may thus be handled as groups,such as where a given local access policy governs an entire group ofdata elements.

FIG. 29 and FIG. 30 show aggregation of various entity types, includingusers, system processes and data elements, using capabilities like thosedescribed for each of the entity types above. Disaggregation can beaccomplished by similar capabilities. As seen in FIG. 30, this mayresult in hybrid aggregation groupings, such as grouping a set of dataelements with a set of users and with a set of system processes, orsub-combinations of those.

FIGS. 31, 31A, and 31B illustrate a flow involving generation ofcertificates in connection with the RAMS system. An instancecomputational chain 10504 is triggered when a RAMS event is triggered10302. The computational chain may involve a variety of certificatesassociated with entities, such as user certificates 10403, processcertificates 10404, data element certificates 10405, previous eventcertificates 10961 and previous association certificates 10962.Processes 5009 and mechanism 5008 involved in the generation ofcertificates may be under user control 1110 and/or RAMS control 1111.The computational chain 10504 may account for various system and otherfactor 10633, such as user input, local policy exceptions, global policyfactors, existing policy instances, content-structure factors,computational factors, existing policy exceptions, and/or previousinstance certificates. At a RAMS event start 10503, the RAMS system maygenerate certificates for association rules for data elements at a step10953A and may assemble participating entity certificates at a step10401. For a RAMS instance 10505, the RAMS may mediate access 10964 forthe instance, create an event certificate 10303 and execute a RAMS event10304, resulting in an instance certificate 10965. In a RAMS eventsequence 10305, a RAMS instance certificate 10966 may be generated(transiently or permanently) and may be retained and indexed, such asfor future reference, such as in a store 10967 of previous RAMS instancerecords and certificates. A facility of the RAMS 10964 may manageinstance occurrences. Instances may be assembled for various timeintervals 10967, such as on schedule, asynchronously, and/or on-demandand may be event-based, time-based, or both. Note also, that asdescribed in the foregoing, RAMS instances may be executed and thevarious certificates instantiated (as described in the examples above)at irregular intervals but further, in variations, may be arranged inirregular intervals as shown in the timeline referenced by callout 10968(instances may be arranged in time- and/or event-based sequences and mayoccur at irregular intervals).

Referring to FIG. 32 and FIG. 33, in embodiments, the RAMS system mayinclude and operate upon one or more object-based, dynamic,access-control and access-permission objects, each referred to as aninstance policy object. An instance policy object may containinformation from local access policies for each of user access policies,system process access policies, and data element access policies thatare involved in a given situation (i.e., based on the users, systemprocesses and data elements involved in an event that triggers or ishandled by the RAMS). This content may come from the user objects,process objects, and data element objects described above (includingtheir ancillary data, including local access policies for each).

Referring to FIG. 34, the RAMS may include components that manage statusfor objects of each of the object types, including user objects, processobjects (including for managing the operational status of processes) anddata elements. These processes may feed processes that manageaggregation and synthesis of instance access policies.

Referring to FIG. 35 and FIG. 36, optionally based on various optionalinput factors, including user input, exceptions to local policies thatare handled by the system, global policy factors, the nature of existingpolicy instances, content-emergent factors, content structure factors,and global exceptions), elements of local access policies may be used toassemble an instance policy object, which may occur entirelyautomatically (by a computer program, such as based on data, context, orthe like, including in an inductive process) or with human intervention(such as via a computer-based interface to a RAMS system). Assembly maybe implemented by various mechanisms, including application programminginterfaces, user interfaces, and the like. This may include applyingelements that contributed to prior instance policy objects (includingapplicable exceptions), as well as applying current instance parameters,such as ones that may indicate a variation from previous instances. Onceapplied, these allow the RAMS or a user of the RAMS to manage a currentinstance policy object, such as for a given instance associated with agiven event.

Based upon the foregoing, in RAMS-enabled environments, entities maygrant and receive “multilateral” access to other entities on a “mixedpeer” communication basis, where “mixed peer communication basis” refersto a fungible hierarchy that may (at one extreme) possess a “flatprecedence geometry” and in another limit, may have a stricthierarchical geometry. A flat precedence geometry refers to schemawherein constituents are unordered and (in this context) entities maycommunicate without limit with any other constituent and withoutreference to any nominal ordering, inter-constituent protocol orpeer-based precedence. By contrast, a “strict hierarchical geometry”refers to an arrangement wherein constituents are constrained to operatewithin a set of hierarchical rules, such as where certain entities havepriority over other entities. A “mixed” arrangement encompasses optionalre-ordering such that, in conditions, peers and groups of peers may beordered (into, a hierarchy, for example), thus constraining inter-peercommunication. “Multilateral” refers to situations where the peers arehighly (optionally massively) interconnected and may communicate withmore than one peer, such that in the limit any peer may communicate withany other.

FIG. 39 illustrates the exchange of publish-subscribe certificates amongentity publishers and subscribers, each of which may include users,system processes, and data elements. Publish-subscribe certificates mayprovide evidence for or certify, for example, access control rights fora given user, system process or data element.

FIG. 40 illustrates elements for an embodiment of the system thatprovides for management of propagation of publish-subscribecertificates. Management may be by one or more centralized processes andmay be performed based on global propagation parameters, which in turnmay be managed using one or more mechanisms, such as by an API, anassisted API, or via a human using a user interface and via one or moreprocesses, such as automatic or programmed processes, a human using auser interface, an automatic process assisted by a human, or aninductive or data-driven process.

FIG. 41 illustrates elements for management of dissemination ofcertificates based on global propagation parameters and based on entitypeer-based certificate management, where entities, such users, systemprocesses and data elements may manage respective certificates in apeer-based system that involves communication and interaction amongthem.

FIG. 42 illustrates the association of associated reference data with anobject. Associated reference data may include object access propertiesfor the object and other information. Associated reference data,including access properties, may be associated with the object and, inembodiments, may be associated with elements within the object, suchthat access properties may be varied even among the elements within anobject. For example, access properties for a header or metadata elementwithin an object may be different than access properties for other datawithin the object. A consolidated set of associated reference data maythus include element-level associated reference data and object-widereference data.

FIG. 43 illustrates associated reference data for child nodes of a nodein an embodiment. Associated reference data, such as access properties,may be associated with nodes of a hierarchy, such that each node mayhave a unique set of associated reference data.

FIG. 44 illustrates an embodiment involving modification of AccessControl Logic (ACL) parameters and associated reference data for anobject. As noted above, an assembled object may have associatedreference data that in turn may have access properties for the object.The object may be associated with a node in a hierarchy or network,which in turn may have node-associated reference data, such as includingaccess properties for the node. Access parameters and associatedreference data (including access properties for nodes and objects) maybe managed and modified, such as based on a variety of parameters,including global parameters and object-specific parameters (which mayinclude node-specific parameters, user-associated parameters,content-associated parameters, and context-associated parameters).Management of modification may be by various mechanisms and processes,including by one or more application programming interfaces, by a humanusing a user interface, by an automated process, by an inductive ordata-driven process, by a human-assisted automatic process, or the like.In embodiments, associated reference data may include a node accessmatrix that stores associated reference data, including access policies,for objects associated with the nodes of a hierarchy or network.

FIG. 45 illustrates a system role hierarchy, where nodes correspond toroles in an organization, for which various levels of access may beprovided. Greater or less permission may be given to users in thehierarchy, ranging from a basic user to a team leader, a projectmanager, a manager and a system administrator. Users can be grouped orassociated with one or more teams. Associated reference data, includingaccess policies, can be set for each node in the hierarchy, where a noderepresents each member of the role hierarchy.

As described in the foregoing, methods, procedures, and interfaceswithin and accessible to RAMS may, in variations, be dynamicallyconfigured (or, in some contexts, dynamically reconfigured) to operatewithin and/or in conjunction with a variety of hardware and softwarearchitectures and environments, including but not limited tocombinations of integrated, semi-integrated, as well as fully orpartially distributed computational environments, and/or withinstandalone, non-networked, integrated, networked or semi-networked,and/or fully or partially virtualized computational spaces (any of whichmay at various points be dynamically or statically so-configured). Inany such variation, the methods, procedures, and interfaces composedwithin RAMS (and other similar such facilities that may be accessible toRAMS and/or which may be utilized by RAMS) may exhibit and may implementsuch adaptive dynamic configurations in numerous ways, including but notlimited to: (i) invocation of methods of adjusting or modifying one ormore operating and initialization parameters; and/or (ii) one or moreprocedural structures; and/or (iii) memory-based or derivedconditionals; and/or (iv) elements within one or more interfacestructures, where any of all such adjustments, in further variations,may be instantiated temporarily or as semi-permanent or permanentchanges, where, in other variations, one or more such modifications maybe (permanently and transiently) grouped and indexed as a group, andwhere one or more so-indexed single modifications and/or groups ofmodifications may be stored and accessed on-demand. Invocation of any ofthe foregoing, including access and invocation of stored adjustments (asdescribed) may occur in response to one more operating conditions, inresponse to internal and/or external factors as well as to current,past, and future characteristics as may be composed within or referencedby one or more subject entities within a RAMS-controlled event orsequence, including but not limited to: (i) entity content; and/or (ii)entity structure; and/or (iii) entity type; and/or (iv) in response tothe computational context within which the RAMS-referenced event orsequence may be executed; and/or (v) in response to one or more user orsystem mandates. Such adaptability, in variations, may extend as far asfungible transformation of methods, procedures, and interfaces (andcomponents and constituents therein) where such transformation may bepermanent, semi-permanent, and/or temporary.

This flexibility provides RAMS-based activity with a variety of layered,dynamic, automated, semi-automated, and human-assisted/semi-automatedcompatibility mechanisms and, in further variations, may provide themeans to implement optimization strategies suitable for parallel and/orsimultaneous deployment in diverse operating conditions and environmentsas well as within heterogeneous hardware and firmware executableframeworks as well as in microcontroller-based execution spaces. Invariations and in some applications, the parameters that may impact suchdynamic, layered, and conditional orientation (or re-orientation) ofcomputational, procedural, and interface components within RAMS may bepre-set (or pre-parameterized) by one or more users and/or by one ormore local or remote system facilities, but, in further variations,combinations of such explicit choices may be joined with or may beoperated in conjunction with both operating condition-dependent factorsand/or with operating condition-independent mandates, invocation casesthat may be based upon or result from one more interfaces or other inputmethods.

In variations, RAMS executable components may optionally execute suchadjustments (transiently or permanently) “on-the-fly”, and suchexecution-sensitive orientation (or re-orientation, parameterization orre-parameterization) may, in implementations, be based upon one or moreaspects within or referenced by numerous factors within or referenced bythe subject computation including but not limited to: (i) relevantentity content; and/or (ii) entity structure; and/or (iii) entity typeand/or (iv) computational or identity-related context; and/or (v)logical or inferred conclusions based upon one or more internal orexternal conditionals; and/or (vi) user or system mandates; and/or (vii)other local or remote inputs or other internal or external operationalstates. Moreover, in some conditions, such adjustments may be global,impacting the entire array of entities within a RAMS-control fabric butmay also be local, applying only to one or more sub-sets of RAMS-enabledentities or one or more groups of sub-sets of such entities, where suchlocal and “regional” adaptivity may emerge (and in some conditions,revert) as a result of any or all of the previously described forms. Insome variations, parameters invoking and/or resulting from such dynamicadjustment may be integrated within the previously described RAMSassociated information, and, in some embodiments where APM may co-existwith RAMS, may be indexed to or otherwise associated within the DTSEntity Management Fabric, noting that, in some cases, such changes maybe optionally included with and/or managed by the identity managementservices provided therein.

The integrated operational flexibility provided within the RAMS-basedcontrol fabric and its distributed capacity to dynamically adjust tolocally diverse and locally changeable and independently variableoperating conditions (as exemplified in any of the foregoing (example)execution configurations) may, in variations, serve to extend RAMS-basedcontrol across multiple heterogeneous operating environments, therebyproviding a unified, cross-environment (and possibly cross-device)entity control skein. In variations, therefore, one or more functionalelements composed within the RAMS control fabric operationally unifydispersed and distributed RAMS-controlled entities, thus forming anintegrated communication fabric among and between distributedRAMS-eligible entities, a distributed execution platform capable ofspanning a variety of devices and execution environments through variousinterconnection schemes. This interoperative communication capability issupplemented by, is coincident to, and in variations, is interactivewith the previously described operational adaptive flexibility,providing a locally-sensitive, locally-responsive, cross-device, andcross-domain inter-operable control exchange layer in support of any orall of the described RAMS functionality.

In embodiments, therefore, distributed RAMS-eligible entities (andentities subject to one or more elements of RAMS-based security fabric)may be arbitrarily composed within, may reside physically or virtuallywithin, or may be accessed as an operating component of, or may bepartially or fully otherwise dispersed or distributed throughout notonly integrated but separate, discrete computational units, where, insome variations, both the integrated or discrete nature of such unitsmay be physical or virtual, or a combination thereof, and such that oneor more sections of any such unit may be transiently, permanently orconditionally accessible via one or more network or sub-net connectionsand/or within and/or by means of one or more operating componentsprovided by or as part of one or more virtualized computational spaces.

Moreover, in the same manner that, in variations, RAMS-based methods,procedures, and interfaces may dynamically adapt to diverse, changeable,and variable operating environments (as described above), the describedcommunication mechanisms that transmit RAMS access control informationto and between entities embedded within such diverse distributedenvironments (as described) may also implement the same degree oflocally-sensitive and locally-responsive adaptability to changing globaland/or local conditions. Such variability may be present in collectionsof interconnected heterogeneous discrete, virtualized, or networkedhardware, firmware, and/or software devices and systems, as outlined inbut not limited to those referenced in the foregoing descriptions.

In embodiments, therefore, RAMS may provide the means to integrate,manage, and to otherwise provision numerous information propagationconfigurations, including, but not limited to, such varied communicationtechniques as unidirectional, bi-directional, and multi-casting schemes,where such protocols may be further enhanced to enable and optimizepropagation and dissemination of RAMS-based control information across,within, and throughout diverse and discrete operating platforms anddevices. Any or all of the variegated informational structuresassociated with a given RAMS event-driven (or condition-driven)permission mediation action (however such mediation may be configured,and by whatever means it may be communicated) may, in embodiments, bestored within one or more data storage structure options, where suchchoices may in some instances, depend on the current or local operatingenvironment. On demand, RAMS information may then be communicated to,amongst, and between local as well as distributed RAMS-based entitiesusing any or all of the means described in the foregoing. In variations,therefore, RAMS may at any time and in response to variable conditionsemploy assorted communication modalities that may leverage variousstorage-based information access configurations including but notlimited to: (i) storage within and dissemination from one or morecentralized repositories (which, in variations, may be physically orvirtually co-located with one or more subject events and entities); (ii)dissemination from one or more distributed “local” or “regional”repositories (where any or all such repositories may, as in theforegoing, be fully or in part physically or virtually co-located withone or more subject events and entities); and/or (iii) disseminationutilizing one or more peer-based communication and/or inter-entitymethod-based information propagation architectures wherein the relevantcontrol information may be passed directly (or in variations,indirectly) among and between RAMS-controlled entities where in suchvariations, the relevant information may, in one non-limiting example,be passed through one or more collections of mutually interlocking andcontext-optimized formatting and interface procedures that may beattached to or associated with one or more entities (and/or may bereferenced by or indexed to or may be interposed between any of the RAMSassociated information, as described previously).

In such peer-based or peer-oriented, direct inter-entity communicationenvironments, RAMS certificate storage and dissemination may, in somevariations, also use numerous information exchange protocols includingbut not limited to: (i) identity-based, address-based, and/or otherentity-specific packet-switched communication procedures; and/or (ii)peer-based or grouped-peer-based identity-specific communicationconventions; and/or (iii) global, regional, or unit-based broadcasttechniques, which may include (but may not be limited to): unicast,bi-lateral-cast, and/or multi-cast propagation. In RAMS-baseddistributed communication schemes that use peer-based or peer-orientedarchitectures, such dissemination techniques may optionally (and, as inthe foregoing, transiently in response to one or more conditions)utilize such example inter-entity propagation methods aspublisher-subscriber techniques, subscriber-only broadcast, andmulti-cast protocols as well as other similar inter-entity informationexchange practices, such as may be deployed in parallel or meshcommunication environments.

In this context, therefore, recall that the DTS Entity-Management Fabricmay subsume, in variations, both RAMS functionality as well as therelated (but not inter-operationally required) APM (AssociationalProcess Management) identity management services. Apart from theassorted functionality provided by and within the methods and procedureswithin and enabled by these entity-centric fabrics, one characteristicthat is relevant in the current context and, in particular, with respectto the security layers built into RAMS-based access control, centersabout an assumption of the validity or legitimacy of the identity ofentities embraced within RAMS-sanctioned events.

Identity authentication assumes a unique position in RAMS architecturefor a number of reasons but in the present connection, mainly becauseentities (users, computational blocks and data elements) arefunctionally equivalent in many RAMS-specific levels but in thiscontext, most relevantly at the associational and aggregation level(though it will be appreciated in light of the disclosure that eachentity-type is different in certain operational functional respects).Given this “entity peerage” architecture and the many ways in whichRAMS, in variations, leverages this novel capability (as outlined in theforegoing), the authentication of entity system-level identity assumes akey role in its security scheme. Identity authentication in thisconnection not only requires that “entity 1” is actually “entity 1” (insystem terms) and that the cryptographically-protected access policyassociated with “entity 1” is a valid association, but that “entity 1”is also of the entity-type it claims to be. In the latter case, forexample, given RAMS-based “entity peerage”, a data element may falsely(and possibly maliciously) be misidentified as a user, and theconsequences could represent a security breach of a RAMS-triggeredevent.

In a simple, non-limiting example that utilizes a variation ofRAMS-based activity, one that generally illustrates the role of entityidentity validation, assume the following: 1) process components C₁ andC₂ are indexed as a RAMS-sanctioned and indexed entity group, C₁₂, and,as described, possess a cryptographically-protected, RAMS-generatedProcess Aggregation Certificate (sometimes called an entity associationpolicy), CI₁₂, as described in the foregoing; 2) assume entity-type userU₁ initiates action to perform computational operations reposed withingroup, C₁₂ where these operations are to be performed upon data elementD₁. As described previously, in variations, this action by user U₁triggers a RAMS-event and, in this example, the RAMS fabric generates aninstance policy for this event by assembling and analyzing the accesspolicies associated with entity-type user U₁, entity-type data elementD₁ and the entity association policy CI₁₂. Assume further that theresult of the RAMS mediation of the rights within these policies isconfirmation that the subject entities indeed have rights to participatein this sequence, and further that this mediated result is embeddedwithin cryptographically protected instance certificate, I₁. Note thatin this simple example, one underlying assumption is that each of theparticipating entities (C₁₂, U₁ and D₁) are indeed those entitiesassociated with the policies submitted for mediation, and therefore thatthe mediated permission embedded within instance policy I₁ is valid forthe current RAMS triggered event sequence. If any of the entities are,through some malicious (or even benign) condition, falsely (orincorrectly) identified in the mediation process, it will be appreciatedin light of the disclosure that this nominally RAMS protected event mayrepresent a compromise of the system. Suppose, for example, that dataelement D₁ is actually a different data element but is incorrectlyassociated with the access policy of D1. Or suppose the entity named D₁(indexed to the access policy for D₁) is actually a user or processassuming the identity of D₁. Such an imposter may request that RAMSspawn an association and the result may be the creation of an entityassociation policy based upon false assumptions. There are, of course,multiple ways to attack any security system, and RAMS is no exception,and while this simple example is illustrative of one type of RAMSvulnerability, it does indicate that the veracity of entity identity isan important element in the integrity of RAMS control fabric. Suchentity identity masquerading and other attack vectors may tend to emergein certain environments, especially within the cross-domain andcross-device environments described in the foregoing, but those skilledin these matters may see that such threats may also be present in anyintegrated, distributed, or virtualized environment.

In the RAMS operating sphere, validation and legitimacy of the identityof entities are relevant in at least two primary ways: 1) thesystem-legitimized identity or unique instantiation of entities; and 2)the right of such validated entities to communicate, where this“doorbell” access right (a type of “ask-before-contact” capabilityrequirement or a “first-principle right to communicate” protocol) is, inthe context of a RAMS-based control fabric, considered to be separateand distinct from the broader array of access and computational rights.Note that in RAMS, misappropriation of an access policy from one entityto another is considered a special case of an entity identity fault.

in the first case, identity validation and authentication of legitimacyrequires some type of verification of the actual (that is,system-defined) identity of a RAMS-managed entity. Note, therefore that,irrespective of whether a set of RAMS-controlled entities (or group ofentities) coexists with an APM structure where, as discussed, RAMS doesnot require APM, and nor does APM require RAMS, the advocacy of theveracity of identity is neither inherent in, nor is it required in, allvariations or embodiments of RAMS. When implementations of RAMS whereinsuch verification requirements are present, in embodiments, RAMS mayrely upon one or more external processes or facilities to validate or toprovide authentication that the nominal or stated identity of an entity(as submitted in the context of a RAMS-controlled event or sequence)actually comports with its “true” identity (as may be defined in thecontext of the system within which RAMS operates). Note, however, thatin other implementations, one or more aspects of identity verificationmay be integrated within a RAMS event, and that in such cases suchvariations may include “traditional” authentication techniques (asoutlined in the following) but, in further variations, may include novelRAMS-based solutions. The methods, procedures, and interfaces associatedwith these variations are most evident not only in the initial identityvalidation but in authentication required in ongoing andcontext-sensitive RAMS-based execution sequences and/or events.

There are many examples of processes that may satisfy even the moststringent identity validation requirement, and which may comprise one ormore elements in any of the previously described validationvariations(including those integrated within RAMS). Such validationprocesses include but may not be limited to bio-metric hardware andsoftware, multi-factor authentication, and hardware-based “dongles”, toname merely a few. Any such procedures may be included within a RAMSfabric, or, in variations, may simply be accessible to suchimplementations through one or more interfaces. Note, however, that suchvalidation techniques are usually applied to users, machines, computers,or even to programs. Most of these methods are typically utilizedinitially to gain access to these machines, computers, programs, etc.and are not generally deployed or required in the course of the types ofevents and configurations covered within RAMS-triggered event sequences.Moreover, these techniques are not typically employed with respect tothe activities in which RAMS-covered entities engage within the APMand/or RAMS fabric. Further, in many situations, especially in thecontext of APM and/or RAMS-controlled events, identity validation oftenrequires positive “in-the-moment” responses, including, in non-limitingexamples bi-lateral, response-required communication, or other userresponse-based modalities that are not always practical or feasible inthese “in-the-moment” required responses.

The second aspect of RAMS-based validation and legitimacy is related tointer-entity communication rights. Methods and procedures that managethis aspect may be present in some but not all variations of RAMS, butmay also be conditionally invoked within certain operating environmentsdescribed in the foregoing. Specifically, this RAMS-based (orRAMS-enabled) facility may be seen as a type of “first-principle”validation which authenticates the ability (or permission) of aRAMS-sanctioned entity (or group of entities) to communicate and/orassociate with another RAMS-sanctioned entity or groups of entities.This layer is separate and distinct from the previously described accessrights mediated by the RAMS fabric. Rather, this RAMS-enabledcommunication security layer may be termed a “doorbell” securityconsideration, an “ask-before-contact” capability or a “first-principleright to communicate” protocol. This feature may be optionally presentand, if present, optionally active in some RAMS implementations and insuch variations, may be waived by any number of means or may be vitiatedby other functionality. But in other instances, variations orenvironments, the “ask-before-contact” capability may be permanentlywaived or may not be required at all. There are many possible reasonsfor this “doorbell” requirement, including but not limited to protectionagainst modes of attack involving certain types of “man-in-the-middle”techniques, and variations of certain masquerading attacks. These andrelated types of vulnerability may be more prevalent within thepreviously described distributed, heterogeneous, cross-domain,multi-device, and possibly hostile or not always fully secureenvironments within which entities may be arbitrarily distributed, asdescribed in the foregoing. Thus, in distributed and heterogeneous andadaptive operating environments, as described previously, the“first-principle tight to communicate” may, in embodiments, provide anovel security layer unique to RAMS.

To illustrate this RAMS feature, assume that: 1) there is anidentity-authenticated entity (“originating entity”) that wishes tocommunicate with a similarly identity-authenticated entity (“receivingentity”); and 2) that the access rights assigned to both the originatingand the receiving entities are encrypted and reposed within theto-be-communicated structure (in this case, a certificate), one that, inthe normal course of RAMS-event sequencing may be transmitted by one ofthe previously cited means from the originating entity to the receivingentity (and in variations, bi-directionally). Thus, in embodiments ofRAMS wherein the “right-to-call” is not active or present or is in somemanner vitiated, the originating entity, in response to aRAMS-sanctioned event or sequence, simply routes its certificate to thereceiving entity by any of the broadcast and/or communication meansdescribed in the foregoing, noting that such facilities may be subjectto any of the environmental operating condition variability andresponsiveness also described previously. Upon receipt by the receivingentity, the RAMS control fabric mediates the request and, based upon themutual relevant array of access rights, a resolution is reached.

In variations and in some conditions where the “right-to-call” may bepresent, required, and active, a further validation may be required inorder to establish and authenticate whether the originating entity iseven permitted to attempt to access the receiving entity in the firstplace, even if that submission comprises an attempt to deliver thesecure certificate. But note that, in this example case, the requiredinformation cannot be accessed since, in a procedural paradox, the RAMSfabric controlling access to the receiving entity cannot determinewhether the originating entity has the right to even try to communicatewith the receiving entity because the information that mediates thatright is hidden in the encrypted certificate.

This example paradox and other procedural barriers that may emerge byvirtue of the presence of a “right-to-call” requirement may, inembodiments and in some operating and computational contexts beaddressed and resolved by one or more definitional solutions whereinthis first-principle “doorbell” access right is treated as a specialcase of an ordinary access right, where “special” in this context meansthat its resolution comprises a prerequisite for other actions and, assuch, functions as a gatekeeper, conditionally qualifying initiation ofother mediation activity. In some variations, such antecedent rightsmediation may be accomplished by invocation of one or more RAMS-based orRAMS-enabled methods and procedures and interfaces wherein an actualuser or system response is required; in other variations, the “doorbell”may be satisfied by secure access to one or more data stores (asdescribed in the foregoing) where such access and such information maybe specifically configured and/or enabled for such gatekeeping purposes.

In other embodiments, however, such solutions may not always be possibleand or feasible. This inadequacy may, in some cases, be due to timeconstraints, efficiency requirements, or even to user inconvenience. Butin embodiments and in some contexts within some variations, suchprocedural issues may alternatively be addressed and resolved throughRAMS-based methods and procedures embedded within (or available to) RAMSthat may be invoked if and when such paradoxes emerge. In some suchvariations, as but one example of many possible solutions, the“right-to-call” access rights would be encrypted separately from thelarger access rights information. Thus, the parameters required toactuate RAMS mediation of the “right-to-call” may first be transmittedfrom the originating entity to the receiving entity (utilizing one ormore of the example broadcast protocols cited in the foregoing) under aform of commonly-known public-private key exchange. In furthervariations and refinements, such antecedent “doorbell” techniques mayadditionally deploy one or more bi-lateral (or multi-lateral) “ack-nack”(acknowledgment-no-acknowledgment) protocols wherein the subjectentities exchange positive affirmation in the context of multiplepublic-private key exchanges. In such example cases (and variations ofRAMS permits many others), these “doorbell” technique solutions, howeverimplemented and however resolved in practice, establish or disallow theright of vulnerable entities to communicate, and thus satisfy theprerequisite security requirement.

The methods, procedures and interfaces described in the foregoingprovide example variations that illustrate (without diminishing thepossible utility nor foreclosing possible applicability and potentialsuitability of other or additional or supplemental methods) the means bywhich RAMS may deploy one or more cryptographically secure“micro-information” exchange protocols to address not only the entityidentity and “right-to-call” challenges, but other security issues, aswell. In embodiments, these and other such techniques may be integratedwithin one or more RAMS control fabrics and are generally applicable toany RAMS-protected environment. In variations, these “micro-information”exchange protocols may be variously parameterized and configured toprovide a variety of levels of protection, and in this sense, may beuseful in even relatively secure, homogeneous environments. In lesssecure environments, however, deployment of more stringentparametrization (and in variations, the addition of other RAMS-basedmethods) may provide a desirable security stratum and a higher degree ofprotection, where such enhancements may be appropriate in operatingenvironments where the integrity of RAMS-protected entities may beespecially important. But further, irrespective of the sensitivity ofany aspect within a RAMS-embraced system, the described RAMS-based“outer” wall, main-message-antecedent information exchange techniquesmay provide a suitable component in RAMS-protected distributed,heterogeneous, locally variable and distributed networks andsub-networks, as described above. Note, however, that in embodiments,RAMS-based methods, procedures and interfaces that may provide controlover or operate upon the parametrization and operation of these“micro-information” exchange routines may, in variations, additionallyincorporate any or all of the previously described adaptability andresponsiveness measures and techniques, where such techniques may relateto and may be responsive to change within (but may not be limited to)any or all of the following: entity content, entity structure, RAMSancillary data, computational context and possibly variable operatingconditions factors, as well as emergence or detection of threat, attackand other operational fault, where such “security alerting” processesmay, in variations, be integrated within RAMS and/or may be otherwiseaccessible to or otherwise communicated to RAMS and/or RAM-enabledmethods, procedures and interfaces.

In embodiments, the security techniques described in the foregoing maybe utilized to supplement, enhance and extend the inherent securityprovided for RAMS-subsumed entities, but, in addition to deployment ofone or more of the cited (or non-cited but appreciated in light of thedisclosure) example techniques (for instance, the public-private keyexchange solution mentioned as an option in the “right-to-call”sequence), alternatives techniques may, in some implementations, proveefficacious. One such alternative is called “DTS entity chaining” (whichmay be equivalently referred to as “entity chaining”, “DTS chaining”,“chaining” or “enchainment”). The following description of DTS entitychaining represents but one embodiment out of many possible variationsand is framed, for the purposes of illustration in terms of two aspectsof RAMS as described in the foregoing: the “entity peerage” architectureand the event-driven structure. Note that many other descriptive framesof reference could be equally valid, and the present pedagogical choiceshould not imply loss of general applicability of methods and procedurescomposing DTS entity chaining (and variations appreciated in light ofthe disclosure) to other systems and contexts.

In the same spirit, DTS entity chaining will also be described below interms of the two security hazards described previously, namely, theentity identity and the “right-to-call” hazards, but note, as well, thatthe choice of these particular hazards is arbitrary and that thesethreats have been selected for convenience and purely for the purpose ofillustration, and thus, the application of DTS entity chaining to theseparticular instances should not foreclose general applicability to othercontexts. In this context, again for the purposes of explaining DTSchaining, the following description will cite only one of the manypotential hazards that may arise in connection with the entity identityand “right-to-call” issues, a threat vector here called “masquerading”.In masquerading, the following description deals only with two cases,both malicious (though many other manifestations are possible): 1)“entity 1” is not actually “entity 1” but has misappropriated theidentity of entity 1, including any associated information (as in theforegoing) but in so-doing, attempts to pose as the same entity-type (asa user, for example); 2) “entity 1” is not actually “entity 1” as in theforegoing but is posing as a different entity-type (substitutingentity-type “user” for entity-type computational process, for example).

Note, however, that, in general, responses to masquerading most ofteninvolve utilization of one or more of the authentication techniquescited in the foregoing (such as two-factor authentication or biometricinput). But since the following discussion is centered around DTS entitychaining, for simplicity and focus, it will be assumed that each entityhas been so-authenticated at least once. Thus, “entity 1” will have beenfully validated to the current required level of reliability, both withrespect to its nominal system identity as well as its entity-type. Asdescribed in the foregoing, each such entity has an associated EntityCertificate which, in embodiments, may be cryptographically-protectedbut further, the two aspects of identity (system-level identity andentity-type) may be additionally encoded, obfuscated and protected.

Note further that, notwithstanding the choice of RAMS-based features oraspects to frame descriptions of methods and procedures within DTSentity chaining, the techniques described (and the numerous variationsthat may not be specifically mentioned but which may be appreciated inlight of the disclosure) may be applied in numerous non-RAMS settingsand environments, and further, may find additional application innon-security computational and data analysis contexts.

As may be the case in many security-sensitive environments, some levelof authentication of the identity of each RAMS-embraced entity is often(but not in all implementations) a requirement within RAMS, and theminimum required level of validation may vary, not only as a function ofand in relation to the requirements of a given instantiation,installation or application (and in variations, in relation to the oneor more entities) but may also be adjustable in light of any systemand/or operating condition variabilities cited in the foregoing, wheresuch adjustability may, in further variations, be dictated dynamicallyor in response to changeable or changing conditions.

While such veracity is often a prerequisite in many other systems, RAMSoperations may differ given the nature of its “entity peerage”architecture wherein users, computational processes and data elementsare treated as equivalent objects. This difference is in part due to thefact that, as a consequence of the lack of clear-line distinctionbetween entity-types, certain protection layers are easier to implement(at least in some embodiments) and at the same time harder to “spoof”.These characteristics may be due to certain properties inhered in thisequivalency, including but not limited to the fact that some conditionsand/or relationships between entity-types should never be possible undernon-malicious operating procedures, and attempts to actuate such thingsmay be detectable. Further, RAMS-events and RAMS-embraced entitiesinteract according to a knowable set of parameters and rules and, thus,the by-products of legitimate interaction will create and follow certainrepeatable patterns, repeatable, that is, within some set of parameters.In variations, such parameters may be mandated or may be inferred or maybe derived over time and/or event lines and/or sequences. In onenon-limiting example, since entity-types are equivalent, let entitieswithin a system be configured as nodes in one or more (possiblyconnected) graph structures (or alternatively, as nodes within one ormore neural net structures). Over time and event sequences (theinstantiation of which may, in variations, become quite complex byincorporating, for example, one or more conditional tests and otherinternal or external information), a set of patterns reflecting theinteraction of those nodes will evolve, and a “normal” (i.e.non-malicious) pattern will emerge. Deviations from such patterns couldtrigger security alerts, and remedial action may be taken. In variationsof this pattern detection, behavior emergent architecture, InstanceCertificates that result from successfully mediated RAMS-events may beused in place of entities, but in other variations, such elements mightbe combined. In embodiments, therefore, the “democracy” amongst users,data elements and processes may be leveraged by methods and procedureswithin the RAMS control fabric to detect and to vitiate certain types ofmalicious activity.

In embodiments, upon initiation of a qualified RAMS-event sequence, theaccess rights of participating entities may, in variations, be encodedwithin an Instance Certificate (and optionallycryptographically-protected), as described in the foregoing. Aspreviously noted, an Instance certificate may, in variations, encode agreat deal of information about the subject RAMS-event, including butnot limited those detailed in FIGS. 31A and 31B. As also noted in FIG.31B, RAMS may, in variations, retain and store Instance Certificates(see data store 10967 in FIG. 31B), and in so doing, will create anindex (and optionally a name). In a further variation of this “logging”operation, the index itself may be cryptographically-protected, encodedor otherwise obfuscated, and in some implementations, this encryptedindex may form all or part of a shared secret distributed to oraccessible by entities that are eligible for this access. Note that, inembodiments, optimization and efficiency-promoting techniques may bedesirable since RAMS-qualified events may occur frequently and may ofteninvolve the same participating entities, especially users, and theindexing and storage of Instance Certificates provides one suchoptimization.

Note, however, as mentioned previously, for the present purposes, theauthenticity of each of the participating entities has been verifiedinitially, so that both aspects of entity identity (system-level andentity-type) for each of the participating entities are assumed here tobe authentic. Thus, the validated mediated access rights as well as theauthenticated identity of the participating entities are composed, alongwith other information, within the stored Instance Certificate. Asoutlined in the previous discussion, in variations, upon successfulmediation of access for each of the participating entities, methods andprocedures composed within RAMS may then generate a set of cryptographicproducts, which in the present (non-limiting) example may include one ormore sets of multi-party homomorphic keys (noting, of course, that inother variations, other cryptographic and secure data managementtechniques may also or in addition be used). Each of the participatingentities may be granted an instance of such a key and this, invariations, may serve as one means by which a participating entity mayaccess the Instance Certificate at a future time. In embodiments, oncein possession of such a key, an entity may submit the key to one or moremethods within the RAMS control fabric, a communication that, invariations, may be made “in the clear” or which may use a public-privatekey exchange or which may take place under the rubric of some othercryptographic scheme (such as a secure shell) but which in any caseconstitutes a “request” to access or operate upon or to associate (ortake some other action) in relation to or with one or more of theentities participating in the subject Instance Certificate.

Returning to the previous example involving a set of RAMS entities,recall that computational group C₁₂ subsumes a set of computationalentities C₁ and C₂ where each computational entity represents a set ofmethods and procedures that may be permitted to be logically groupedbased upon one or more process association rules, where, if permitted,the resultant aggregation comprises a sub-set of methods and proceduresdrawn from a larger set of available methods and procedures, a groupingthat may be instantiated, in variations, through user and/or systemmandate or in response to other means and conditions. Recall also thatin managing the association of computational entities C₁ and C₂, methodsand procedures within RAMS generate a Process Aggregation Certificate(sometimes referred to as an entity association policy) which in thepresent example is called CI₁₂. In a manner similar to the previouslydescribed Instance Certificate I₁, CI₁₂ subsumes information includingbut not in all variations limited to information surrounding itscreation, information about the entity identity (and type) of itsconstituents, information about the mediated access rights associatedwith each of the computational entities C₁ and C₂ as well as thecombined access rights now associated with computational group C₁₂.Finally, in variations, as with the previously described InstanceCertificate I₁, the information within CI₁₂, may be encoded and/orcryptographically-protected, and then indexed and stored. Note that inthe present example, the entity identity and entity-type ofcomputational entities C₁ and C₂ have been authenticated.

In the present example, assume that the previously described entity-typeuser U₁ and entity-type data element D₁ participate in the activity inthe prior example, combining with computational group C₁₂ to trigger aRAMS-event, which, as described, results in Instance Certificate I₁.Recall, as well that both entity-type user U₁ and entity-type dataelement D₁ have been authenticated (as described in the foregoing) and,therefore, that all the information composed within and related toInstance Certificate I₁ is similarly valid. Next, as with the previouslydescribed certificates in this example, assume that the informationwithin Instance Certificate I₁ has been encoded andcryptographically-protected, and then indexed and stored. Finally,suppose that the RAMS-event represented by Instance Certificate I₁ isone of n such events, each possessing a unique Instance Certificate,where the nth such event is associated with Instance Certificate I_(n).Assume also that, as in the foregoing, all such Instance Certificates aswell as the participating entities have been authenticated, as in thecase of Instance Certificate I₁ and that each of the n such InstanceCertificates has been likewise encoded, cryptographically-protected andthen indexed and stored. Each of the n Instance Certificates thereforepossesses a unique set of keys which, in the present example, will havebeen distributed amongst the relevant participating entities.

But suppose that at some point in time after the creation of InstanceCertificate I₁ entity-type user U₁ re-initiates the same actionpreviously validated and covered by Instance Certificate I₁. In thispresent example, assume that the nominal entity-type user U₁ is inpossession of the previously generated homomorphic key assigned toentity-type user U₁ upon the creation of authenticated InstanceCertificate I₁ and thus, in the nominal case, entity-type user U₁ isable to execute the associated RAMS-event. But there is no guaranteethat entity-type user U₁ is not another user or even another entity-typemasquerading as entity-type user U₁. In variations, there may be othermethods that may reduce some of the risk in such situations, but suchhazards may not always be fully vitiated.

“DTS entity chaining” represents one solution to this dilemma and acontinuation and expansion of the present example may serve toillustrate an example embodiment of the constituent enchainmentprocesses. Assume that each of the n events cited in the foregoing (andassociated with n Instance Certificates) occurred at some quantifiableand unique discrete point in time within the host system or with respectto some shared-resource clock (noting, of course, the existence of anedge case wherein two events might somehow occur at exactly the sametime, but further noting that in variations, such “collisions” may bedetected and prevented). Suppose further that, in the current example,multi-party homomorphic keys are employed in the access mediationprocess and that methods and procedures composed within RAMS that managethe generation of these multi-party homomorphic keys ensure, for eachevent instance, that each key-set is unique, an operation that, invariations, could use any number of parameters unique to the eventincluding but not limited to time stamp (as in the foregoing), place andcontext in which the event certificate is created. Thus, each key in theset of multi-party homomorphic keys associated with any event would beunique. An entity participating in the nth event would, for example,possess a key from the set that is uniquely generated in conjunctionwith and which is made to operate only with Instance Certificate I_(n).The DTS enchainment process mandates that each such Instance Certificatebe linked to another, where the method of inter-Certificate referencingmay use any number of techniques, some of which may be as simple assingly or doubly linked lists structures but which, in variations, mayalso and/or instead employ one or more encoding and/or cryptographicmethods. In any case, when two or more Instance Certificate areenchained, methods and procedures associated with the DTS entitychaining capabilities ensure that, the order of enchainment remaininviolate, where the specific order may be based upon any number offactors or upon a combination of factors, including but not limited to:a timeline, an event or sequence line, one or more system and/orexternal conditions, one or more elements composed within at leastCertificate or a combination of any or all such factors.

Note, however, that the use of Instance Certificates is but one choicein DTS enchainment. Other variations of DTS entity chaining may also orinstead use one or more elements that may be associated with anyentity-type, where examples of such elements are included but may not belimited to those shown in FIGS. 22-25. In variations, since much of thisancillary information may be unique to a given entity, this data may becollected and filtered and may be made subject to a variety of encodingand formatting processes which may then be used in as an element in aDTS enchainment scheme. The important point in this context is that theentity-type structure within DTS and RAMS (and as may be managed by APM)is rich in entity-specific information, any portion of which may be usedboth to validate identity and/or as a factor in binding the entitywithin a DTS entity chain.

As those familiar with these arts may see, DTS entity chaining isessentially a type of blockchain schema or distributed ledger system,and in variations, DTS entity chaining may bear some similarity to knownand popular “mainstream” distributed ledger architectures. Incrypto-currency systems such as Bitcoin™ and other crypto-currencies, aunit-of-value is spawned as the result of successful completion of acryptographic brute force ‘mining operation’, and this “unit” may beseen as analogous to DTS-spawned entity-related structures, such as, forexample, Instance Certificates cited in the foregoing example. DTSentity chaining, in this example variation is analogously similarcrypto-currency systems in that Instance Certificates may only be placedin an enchained sequence upon successful performance of a task, where inthis example variation, the task is the successful creation of mediatedset of rules and the subsequent creation of a set of multi-partyhomomorphic keys. Thus, in the present example, successful enchainmentof a given Instance Certificate conveys authentication of the event withwhich it is associated; this validation implicitly authenticates itsparticipating entities, as well as the rights conveyed in theCertificate. Therefore, in the context of the present example usingInstance Certificates, if an entity may be successfully (andauthentically) correlated with at least one instance of itself within anenchained Instance Certificates, the identity of that entity isvalidated by virtue of its position as a constituent within a “unit” inthe chain. In embodiments, methods and procedures and interfaces withinRAMS and within the DTS enchainment processes provide the means tovalidate these connections.

In addition, in embodiments of DTS entity chaining, the order and termsof linkage of the enchained certificates may be protected in a number ofways, including but not limited to traditional cryptographic means aswell as various other methods of obfuscation. In variations, theenchained certificates, whether encoded, encrypted or otherwiseobscured, may also be “published” and attached to, distributed to and/orassociated with any of the elements covered within the RAMS controlfabric, and/or other DTS objects and/or in other non-DTS internal orexternal locations, so that tampering with an enchained certificatecould be detected by means typically employed by distributed ledgers.Protection by publication, essentially a type of shared secret, isanother way that DTS entity chaining may be seen as similar to adistributed ledger system.

In embodiments, methods and systems for entity chaining are describedherein and include a wide range of techniques for securing entityinformation found, for example in and through Instance Certificates,Aggregation Certificates, Event Certificates, Associational Certificatesand the like. Other entity information that may be secured may includeEntity Root Certificates and the like. Described below are additionaltechniques for securing information available in the DTS Platform,including entity information and the like.

Variously throughout this disclosure, methods and procedures formanaging access by and to entities to other entities (e.g., user, data,processes and the like), information associated with and/or linked toentities (e.g., the exemplary Certificates mentioned above and elsewhereherein), are described and depicted in accompanying figures. On suchembodiment comprises one or more variations of the described RecombinantAccess Mediation System (RAMS), such as mediation for access by a firstentity to a second entity. An example of such access may includeaggregating two entities into a group or team that, once formed, canperform additional access functions.

Mediation for access produces an outcome that may include a certificatethat may represent an authentication of the requested access (e.g., apair of user entities joining to form a group may be authenticated by anAggregation Certificate generated in association with mediation by thepair of user entities, and the like). As described herein, a certificatethat represents an outcome of access mediation may be generated for anytype of access between and among entities. Additionally, execution ofprocesses within the DTS platform result in an Event Certificate thatfacilitates, among other things, identification and tracking ofparticipating entities, source and generated data, and the like.

Also, as described herein, each certificate derived from a mediationevent, such as mediation for access, is linked to a root certificate forthe participating entity/entities. In a basic example, a user entity mayrequest access to a data entity (e.g., a document). Upon successfulmediation, a certificate (e.g., Instance, Event, and the like) isgenerated and linked to a root certificate for the user entity and thedata entity. If the user entity is an aggregated user entity (e.g., agroup of two or more user entities), the mediation certificate generatedwhen the group is granted access to the document will reference theAggregation Certificate for the user group and the root certificate forthe document entity. The referenced Aggregation Certificate willreference the root certificates for the user entity-members of thegroup. Therefore, a link can be established from a mediation generatedaccess certificate (a group of users accessing a document) indirectly tothe root certificates of the individual user entities in the group.

Securing this multi-dimensional certificate linkage and the content ofthe certificates may be accomplished by enchaining the certificates toform Multi-Dimensional Enchained Certificates (MDEC). Each certificatein a MDEC chain may be encrypted with a key that is shared among theentities identified in (and therefore authenticated to access) thecertificate. The secure access certificate may include information thatfacilitates identifying a next chained certificate, but may not aloneprovide the unique shared key for this next chained certificate. Onlyentities identified in this next chained certificate may be provided theshared key to decrypt it. In this way, access to associational andrelated certificates generated by RAMS enforces entity authentication ateach certificate in an MDEC chain based on a unique shared key for thecertificate. Without this shared key, even visibility to the nextcertificate in the MDEC chain is not accessible.

In an example of MDEC, a first user entity may mediate for access to anowned data entity that is owned by a second user entity. Note that thefirst user entity may have a root certificate referred to in thisexample as U1RC. The owned data entity by the second user entity may bea result of access mediation such that the owned data entity for whichthe first user is mediating access may be represented by a certificate(e.g., an association certificate and the like), called for convenienceOwned Data Certificate (ODC) that links to a root certificate of thesecond user entity (U2RC) and to the root certificate of the source dataentity (SDRC). Upon successful mediation for access to the owned dataentity, a certificate representing this access may be generated (U1AC)and enchained to the owned data certificate (ODC) that is, as describedin this example, enchained to the root certificates of the second userentity (U2RC) and to the source data entity(SDRC). In this example, thefirst user entity may modify the owned data entity, causing, for examplean event certificate (U1EC1) to be generated. This modified owned dataentity may then be published, effectively causing a second eventcertificate (U1EC2), to be generated. In this example, three MDEC chainsare formed:

U1EC2.U1EC1.U1AC.U1RC

U1EC2.U1EC1.U1AC.ODC.U2RC

U1EC2.U1EC1.U1AC.ODC.SDRC

Each MDEC chain terminates at an entity root certificate. Alternatively,a branched MDEC chain of certificates is formed that starts with themost recent event (publishing) and carries down through three branchesto a root certificate for each entity. The MDEC chain startsU1EC2.U1EC1.U1AC. It then branches three ways: (i) U1RC; (ii) ODC.U2RC;and (iii) ODC.SDRC.

Securing this MDEC chain(s) may include using any of the certificatesecurity and enchaining techniques describe herein and may furtherinclude encrypting U1EC2 with a shared key that is securely communicatedto the root entities, namely user 1 entity, user 2 entity, and sourcedata entity. Each certificate in this MDEC chain may be encrypted with ashared key that is shared with the root entities. Additionally, thecertificate for user entity 1 modifying the owned data entity (U1EC1)may only be visible by traversing the MDEC from U1EC2 by firstdecrypting and accessing the content of U1EC2. By applying thischain-following technique, access to, for example an entity's rootcertificate requires multiple dimensions of decrypting with shared keysthat are only accessible by the root users.

In embodiments, an MDEC chain of certificates may facilitate decryptedaccess to a shared certificate that is next in the chain by decryptingan adjacent certificate. In this example, entities that decrypt U1EC2may gain access to a decrypted version of U1EC1, such as by accessing apartial decryption key for U1EC1 in certificate U1EC2. However,accessing U1EC1 in this example may be limited to entities that areidentified in and therefore authenticated to access U1EC1. In thebranching example above, user entity 1 may reveal the partial decryptionkey for ODC when accessing the shared certificate U1AC; however, becauseuser entity 1 is not a party to the owned data and therefore is notidentified as authenticated to access the ODC, user entity 1 may beprevented from accessing the ODC, which may for example revealinformation about the participants in the owned data that thoseparticipants may wish to conceal.

In yet other implementations, the certificates themselves may besecurely shared with the root entities and may represent shared keys fortraversing the MDEC chain from the last event to the root entity. Whileall entities may have access for decrypting to all shared certificates(e.g., U1EC2, U1EC1, U1AC, those certificates that are formed on abranch separate from one or more of the root entities in the sharedcertificates may not have access to these certificates or to the branchin which they occur. In this example, only user entity 1 may have accessto its root certificate (U1RC) but does not have access to the owneddata certificate (ODC) that is formed by user entity 2 owning sourcedata. Both user entity 2 and source data entity may have access to theowned data certificate (ODC) in which each is a participant. However,only user entity 2 has access to root certificate U2RC and only thesource data entity has access to root certificate SDRC.

In some embodiments, DTS entity chaining and any of its variations andderivative structures can also or instead be applied as a componentwithin other distributed ledger system where, in a variation, one ormore Instance Certificates (or other DTS object, as in the foregoing)may be “enveloped” in a blockchain wrapper and may either participate asa unit or may be linked to another such unit within that blockchainstructure. Blockchain employed to secure digital assets, such as digitalcurrencies and the like is generally known as a mechanism for providingimproved security for such assets. However, security of a block chain,distributed ledger and the like may be enhanced further by controllingaccess to the assets and/or mechanisms by which the assets aremanipulated through use of methods and procedures of a DTS platform,such as without limitation access mediation as may be described herein,including in association with a Recombinant Access Mediation System(RAMS). RAMS presents an opportunity to enrich access control that goesbeyond conventional mechanisms such as two-factor authentication, secretkeys and the like through use of access mediation control capabilitiesof RAMS. RAMS employs algorithms that, among other things, determinecorrelations between and among digital representations of aspects ofentities, such as without limitation matches between values representingcapabilities of entities and the like. RAMS mediation algorithms mayperform correlation between two or more entities, such as two usersattempting to perform a digital currency exchange by determiningcorrelation of a range of data that is descriptive of the users, whichmay include activities conducted through or in association with the DTSplatform that may be determined to be associated with the users, such asprior digital currency exchanges and the like.

A DTS Platform 1000 including at least a RAMS portion thereof, mayfurther facilitate block chain security through mediation of one or moreentities and a digital asset being protected by the block chain. Asdescribed herein, each entity, including at least users, data/digitalassets, and processes may be treated equally by mediation so thatelements that describe capabilities, preferences, and history of anysuch entity may be applied in a mediation with another entity todetermine access by one entity to/of the other. In a block chainexample, an entity, such as a user operating a computer may wish toperform a function on a block chain-protected digital asset. RAMSmediation may, in this or another order, mediate access by the user tothe computer, by the user-computer combination to a digital assetmanipulation function, and by a user-computer-manipulation function tothe digital asset. If any such mediation were to produce results thatwere indicative of an unsuccessful mediation, such as a lack of accessrights, the mediated access (e.g., the user-computer combinationexecuting the digital asset manipulation process) could be denied,further ensuring security of the digital asset and/or of the block chainprotection system.

DTS-based RAMS mediation further facilitates security of the block chainprotection system by ensuring that aspects mediated for combinedentities (e.g., a user and a computer) represent those aspects that, forexample, both entities share. Therefore, an entity that does not havecertain capabilities (e.g., certain digital asset access rights) cannotmerely combine with another entity with such rights and therefore gainaccess to the digital asset. In the example above, while a user may haveaccess rights to execute a digital asset manipulation function, thecomputer on which the user is requesting to access the function may berestricted from such access based, for example, on previous activityperformed through the computer that compromised the security of thecomputer. In this way, an unknowing user who accesses an unsecuredcomputer to access his digital assets, such as remotely will beprotected, as will the integrity of the block chain.

Embodiments of DTS-based RAMS mediation that may facilitate protectionof block chains and block chain protected assets through, for example,mediated access to entities of a block chain environment (e.g., users,computers, distributed ledgers, digital assets, functions thereof, andthe like) by using representational domain concepts, such as directedgraphs and the like that are described elsewhere herein. As an example,data representing capabilities (e.g., security and related capabilities)of a user, computer, digital asset and the like may be converted into aplurality of directed graphs (e.g., acyclic graphs and the like) thatmay be collocated so that correlations among the graphs may bedetermined, such as by intersections in the representation domain ofcapabilities of two users seeking to share a digital asset. Ifintersections of the graphs fail to meet an access criterion, then thedigital asset may be secured from being shared (e.g., transferred fromone to the other) by the two entities.

The diverse and broad and pervasive functionality provided by RAMSthroughout a Platform 1000 and the methods and procedures and interfacesthat administer and maintain System Capabilities is reflected in thesimilarly ubiquitous TCM (Topical Control Mediation). As noted, anddescribed in earlier sections of this disclosure, the RAMS and TCMcontrol and mediation capabilities along with the Associational ProcessManagement (APM) services, in embodiments, form the core (though, invariations, not the only) operating elements within the DTSEntity-Management Fabric (E-FM), the suite of functional componentswhich provide an array that support specialized functionality andextended capabilities for DTS entities (users, data aggregates andprocesses). The Entity-Management Fabric (E-FM) binds and integrates andsupports the interoperative functionality of its constituents but also,through interfaces, facilitates (but does not, in instances, fullymanage) the integration of its subsumed functionality with otherelements within the DTS Platform operation environment. In embodimentsand in concrete practice, E-FM is implemented through a collection ofuser- and system-parametrized control blocks and a suite ofinterconnected and distributed interfaces which, as with manyoperational components within a DTS Platform, explicitly integrate theDTS-ubiquitous content-sensitive and content-responsive methods andprocedures which, in embodiments, may be reactive to content types andoperating conditions and may, in context and on demand, dynamicallyassemble one or more appropriate computational elements to be employedto execute its functionality.

In contrast to RAMS which controls and mediates entity associational andexecution capabilities within a DTS Platform 1000 (DTS System Control),TCM provides control and mediation over entity associationalcapabilities (and in variations, execution of certain system-provided orfacilitated functionality) based upon system- and user-defined topicswhich are assigned relationships with each entity-types. This controlskein may, in embodiments, be variably-applicable andconditionally-applied and may be modulated by both users and systemprocesses by means of elicited parameterizations, initialization schemesand other interactive user interfaces which, in many cases may, at userand/system option, variably, conditionally and by degree and in context,exert regulatory control upon an array of control and mediationparameters, ranging in precision and accuracy from highly granular anddetailed to grossly broad and general. Such user and system controloverlays may also gather and elicit input and other relevant informationfrom any sources, information aggregates and/or other resources within aDTS Platform (subject, of course, to RAMS control and mediation andother system-fostered control mechanisms), controls and data flows that,in embodiments may be effectuated through one or more methods andprocedures and interfaces subsumed with or associated with the E-FM, asdescribed previously. In addition to system initialization and directand indirect user input, additional system-based input and modulatinginformation may also, in embodiments, include one or more parametersprovided by any or all of the methods and procedures and interfacesassociated with the previously-described DTS In-App activity logging andmetric functionality (as depicted within block 1107 in FIG. 1 and otherfigures and as described throughout this disclosure).

Despite its moniker and its described focus as being centered upon themediation and control of the capability of one or more entities toassociate with other entities based upon one or more system-definedtopics, TCM centered about a particular class of information associatedwith each entity in a DTS or DTS-enabled system. Referencing FIGS. 15-17and recalling the descriptions of the various information aggregatesassociated with, bound to and indexed to each entity, (where in thosefigures and descriptions, these aggregates are referred to as objects),each entity is assigned at least one such object. In the non-limitingdescriptions presented in previously paragraphs which detail exampleoperational modalities and implementations of RAMS, the information andparameters required for mediation of collective DTS System Capabilitiesassociated with participating entities in a given associational orexecution context are shown in an example representation as in FIG. 22as subsumed within information aggregates shown as blocks. While FIG. 22provides an example configuration of a schematic presentation of suchinformation, details may vary in changing contexts, between differententity types and in variations of these methods. Thus, the examples inFIG. 22 depict the information blocks as they may be configured in oneinstance for entity-type users while other figures and descriptionsdetail optional variations as they may apply in other situations and toother entity-types (system processes and data).

Referencing FIG. 22, note that User 1 10702-A has an information blockUser 1 Object 10703-A, an aggregation of information which is bound toand associated with that user and which, in variations, may beoptionally encoded and/or encrypted and/or otherwise obfuscated. Butnote that information block 10703-A is depicted as composed of a numberof sections and subsections revealed in progressive detail from left toright in FIG. 22. These subsections start with information block User 1Ancillary Data 10706-A which in turn is represented as composed ofinformation blocks User 1 Access Policy Data 10708-A and Other Info User1 10709-A. Note that, as described previously and as announced bycallouts 10707 and 10710 (All Users Possess these Structures andUser-Specific & User-unique Addressable Control and Status Data,respectively), each entity (including all entity types) must possesssuch a structure and that each such structure is unique and specific tothat entity and finally is addressable by one or more elements within aPlatform 1000. While shown as contiguous in these depictions, note thatthis information need not be physically or logically continuous, and invariations, and in consideration of certain system considerations, anyor all such instances for any entity may be discretely and randomlydistributed in randomly variable chunks, and may, in further variations,be independently and/or separately encoded, encrypted and/or reposedwithin various enchainment schemes, as described in the foregoing.

In the present context, however, note that this variegatedentity-associated object contains a variety of information including allthe information, in whatever form and format required for the methodsand procedures and interfaces within the Entity-Management Fabric andits constituents RAMS, APM and TCM) to execute implement theirrespective functionality: System Capability mediation and control(RAMS), Associational, identity, indexing and ownership control (APM)and Topical Mediation Control (TCM), respectively.

The information required for TCM and for RAMS, while related, differ insubstantial and substantive ways but in variations, may be subsumed inthe same repository structure as shown in FIG. 22, for example, asreposed within User 1 Access Policy Data 10708-A. In such an instance,the TCM-relevant information would comprise a separate subsection and,in embodiments, may be distributed within a separate series of databasetables. Other arrangements are, of course, possible. Note also that thenumerous sources of information and variety of types and formats thatmay modulate or may be factors in executing functionality for both RAMSand TCM (and other functionality within a DTS platform) may be reposedhere and/or may be referenced, encoded or otherwise represented. Topicalmediation of entity association is based upon a type of variegatedentity-centric aggregation of information similar to a profile called inDTS-terminology the DTS Topical Capability Profile or (DTS TC Profile orsimply TC Profile). This unique structure forms the core of eachentity's topical identity and defines not only its capability withrespect to a given topic but other parameters that assists in thecomplex associational mediation functionality executed by TCM. Note thatthe example structure of the TC Profile is described in the followingparagraphs in relation primarily in terms of entity-type “users”, butthese non-limiting descriptions and examples apply equally to otherentity types, as well, although, whether and to what extent exceptionsexist for a given entity, such exceptions will be noted. Further, any orall of the DTS-based (and/or DTS-enabled and/or DTS-accessible) methodsand procedures and interfaces that may be used in the process ofcreating, maintaining and executing any aspect of TCM as described inthis disclosure and variations that may be evident to those skilled inthese and related arts, may also apply fully or in part to one or morefunctional elements within RAMS, but as well as to other aspects withinor associated with the E-MF.

The DTS Topical Capability Profiles (TC Profiles) are, in general,germane to a given topic or topics, which, as described previously, maybe defined by platform operators and/or by system processes and/or byusers. Thus, in implementations, there may be many topics managed by TCMso that, as a result, there may be many discrete instances of suchtopic-specific TC Profile structures any or all of which may be attachedto any entity and/or which may be detached from any entity, dynamically,permanently, transiently and repeatedly based upon any internal orexternal condition and/or for any reason nominated by platform operatorsand/or system processes and/or users. Note, however, that, inimplementations, and in contexts, any or all such TC Profile's may becombined based upon any number of reasons, including those listed in theforegoing but also based upon information that may be extracted from oneor more DTS-based (and/or DTS-enabled and/or DTS-accessible)content-sensitive and content-responsive methods and procedures andinterfaces which may, for example, dynamically optimize the operation ofTCM based upon (but not limited to) such things as patterns of systemusage.

In embodiments, DTS TC Profiles may contain variations of one or moredescriptive informational categories such in the following non-limitingexample. In variations, such categories may be defined by the platformoperator and/or by DTS-based (and/or DTS-enabled and/or DTS-accessible)methods and procedures and interfaces within Platform 1000 and/or byusers but may also vary based upon one or more aspects of applicabletopic or topics. Such descriptive informational categories within (orimplied within) a TC Profile may include (but may not be limited to) anyor all of the following in any combination: i) things that an entity is;ii) things that an entity has been; iii) things that an entity can be;iv) things that an entity can do in any of i-iii above; v) given thingsthat an entity can do as in iv) above (in any or all of i-iii) what maythis thing as an entity may do and/or be done upon—that is, given anentity (defined as it currently it is and/or as it may have been in thepast and/or as it may (or can) become in the future) what things canthis entity do and what can it do that thing upon. Note that eachelement may be interpreted in a number of ways within TCM depending, forexample, on such things as the nature of the topic, the context and thepossible mediation modalities.

In a simple non-limiting example, let thing T be an entity-type “user”but as a “user” is an instance of user-category A domiciled in location1 within country C. An instance of entity-type “user” of user-category Acan be in one of a number of places, in this case, place X (the livingroom), place Y (the dining room) and place Z (the kitchen). Let thereare two tasks, task Q and task P and two objects, object O and object M.In this example, let the following country-wide rule be applicablethroughout Country C such that it overrides all local laws: anentity-type “user” of user-category A can only perform task Q uponobject O and can only perform task P upon object M. Thus, in Country A,in no case can an entity-type “user” of user-category A ever apply taskQ to object M and can never apply task P to object O. In addition, letthere be a rule that applies only in location A (but does not applycountry-wide) to all instances of entity-type “user” of user-category A:when entity-type “user” of user-category is in place X (the livingroom), the entity can perform either task Q or task P, when in place Y(the dining room), can only perform task Q and in place Z (the kitchen),can only perform task P. Thus, in the living room (place X), thing T caneither perform task Q or task P (due to the location 1 rule) but ifthing T chooses task Q, thing T can only perform task Q on object O (dueto the country-wide rule) and if thing T chooses task P, can onlyperform task P on object M (also due to the country-wide rule).Likewise, in the dining room (place Y), thing T can only perform task Q(location 1 rule) and (due to the country-wide rule) can only performtask Q on object O. Finally, of course, when thing T is in the kitchen(place Z), thing T can only perform task P on object M but cannot doanything with object O since when in the kitchen thing T is prohibitedfrom executing task Q (due to the location 1 rule), the only taskpermitted for object O. This example can continue but the basic conceptsof the relationship of an entity to its Topical Capability Profiles (TCProfile) are clear.

In this simple case, the enumeration of the rules that govern thecapabilities of thing T are embodied within one or more TopicalCapability Profiles (TC Profiles) and methods and procedures andinterfaces embodied within TCM (and within related and associatedcomputational components within RAMS, APM and throughout a Platform1000) enforce these associational capabilities. Note, however in thiscase, there is only one entity-type defined, entity-type “user”, andonly one instance of this entity-type (thing T) and only one categorywithin entity-type “user” (user-category A). Thus, in this simple butpossible case (one supported by embodiments of DTS and TCM), there isonly one Topical Capability Profile (TC Profile) in place. But inreality, even in simple situations, there may be not only many instancesof entity-types (that is, many instances of entity-type “user”), eachwith a distinct TC Profile, but there may also be other possiblecategories within which any such instances may be allotted (inembodiments, entity-types may have more than one category) and manyadditional conditions (“places” in the earlier example) in which eachsuch categorized instance may be, again requiring more types of TCProfiles (or extending current profiles). Such situations are possiblein DTS environments wherein TCM must integrate a combinatorically largeand possibly growing collection of discrete and different capabilitiesembodied within TC Profiles attached to an entity-type.

Moreover, the present example only enumerates a single entity-type, asituation that while possible and supported by TCM and DTS, is asimplification of the more typical situations in which there may bemultiple entity-types, as, for example, in DTS environments where, asdescribed, there are at least 3 entity-types and there may be morecategories and variations within each entity-type. In the firstinstance, in an extension of the non-limiting simplified singleentity-type example, not only could there be many more instances ofentity-type “users” (such as, for example, additional user-categories Bthrough Z) but any such additional user-category may have manyvariations. Each such user-category type and each variation as well ascombinations of these categories and variations may each have a separateand distinct set of capabilities and thus, a discrete instance of a TCProfile. Further, as mentioned, there could be other entity-types, eachwith their own categories and variations and a separate suite ofcapabilities may apply to each instance, thereby also increasing theinstances of TC Profiles.

The simple case presented in the single entity-type example, and theextensions introduced by the addition of more categories and variations,as well as the presence of additional entity-types provides insight intothe powerful and novel capabilities provided by TCM. In variations,additional properties may be present in a capabilities enumeration whichmay include addenda and extensions based upon but not limited tocomputational and semantic context, subject and state of the one or moreaspects of the relevant content, various states of the constituententities as well as time-based variations and the prior or projectedstates of one or other conditions. Thus, in light of the foregoing andas discussed in the following paragraphs and examples, and throughoutthis disclosure with to respect such operations as the combinatorialmodalities of Assertional Simulation, the many variations andpermutations of topical capabilities within the entity-centricenvironment of DTS and the application of the multi-modal, multi-layeredcontent-sensitive and content-responsive associational mediationexecuted upon these capabilities by methods and procedures andinterfaces within and associated with DTS Topical Mediation Control, isa unique contribution to the current art, one that extends and enhancesthe capabilities of current systems, enabling new and novelcapabilities.

In embodiments, a DTS Topical Capability Profile (TC Profile) mayconsist of one or more sections. Note, however, that in many situations,there at least two such structures, called TCM Properties and TCMCharacteristics and, often, optional additional sections as may berequired in embodiments and with respect to different topics. Ingeneral, but not exclusively, these sections are structured identicallybut contain different nodal values. The following examples anddescriptions depict an example implementation of an exemplary TC Profileusing a particular taxonomic hierarchical structure for three sections:a nested subsumptive containment hierarchy (or a nested partiallyordered set or nested poset), but with the following non-exhaustive andnon-limiting list of rules: i) nodes are comprised of at least a key (alabel) and a value where no children (siblings) of the same parent mayhave the same label (noting that in other examples, additionalinformation and references may be present); ii) a node in the hierarchymust either have a child or must be a child of another node; iii) achild may have one and only one parent (noting that this is a propertyof nested containment sets) and that a label attached to child isnon-unique (another property of nested containment sets) so that eachnode is unique only by virtue of the conjunction of its label and itsparentage—that is, a node must be defined and may only be addressed byusing both its label and its unique path to the apex of the tree; iv)each child of a parent node inherits at least one of the characteristicsof its parent. In embodiments, and in more complex examples, other rulesmay apply but, in all cases, when this form of hierarchy is used, allthe rules of nested containment subsumptive hierarchies apply, eventhose not listed here, but, in some cases, additional conditions andrules may apply.

Note again, however, that the choice of structure as a nestedcontainment subsumptive hierarchy is purely pedagogic and, inimplementations, many other structures may not only be employed but maybe optimal for the content, context and the application of theassociational mediation control provided by TCM. Note also that asmentioned, such structures may co-exist with other structures and theproperties described in the foregoing apply here, as well.

Further, recall that some applications and variations may require or mayexecute or may conditionally permit TCM functionality to be employedusing hybrid or mixed structure Topical Capability Profiles (TCProfiles) and in these instantiations, the structure and content of theinformatic aggregation may be transformed bi-directionally and, ininstances, dynamically in response to, for example, one or morecontext-derived and/or content-dependent state, which any such state maybe ephemeral, transient or permanent, and/or which may result in thesynthesis of a new, discrete instantiation of one or more elementswithin a TC Profile. As may be noted, this dynamic adaptivity is anear-universal property within a DTS Platform 1000 and appliesthroughout TCM as well as within the related APM and RAMS components.

These example modalities indicate the many variations that a TC Profilethat may exhibit, some simultaneously by means of transformative cloningoperations but in all cases potentially combinatorically, based on thedynamic, content-sensitive and content-responsive and context-derivedmutability, variability and transformative nature of this uniquestructure, a property that extends, through the dynamic nature of thisprofiling structure, to the execution of associational mediationprovided by TCM. Note, as well, that any or all such properties may alsobe applied to and used by one or more elements in the creation,maintenance and execution of equivalent profiling structures andcapability mediation as may be present in and RAMS and in variations.

In implementations, therefore, each component element in a DTS TopicalCapability Profile (for example the TCM Properties and TCMCharacteristics components as well as additional structures) may becreated and maintained by the operator of the platform and/or by usersand/or by methods and procedures and interfaces associated with variousDTS-based (and/or DTS-enabled and/or DTS-accessible) system processeswhere the relevant operations may include (but may not be limited to)operations that synthesize, add to and subtract from and which mayotherwise update and maintain the composition and structures withinthese aggregations. Note, as well, that, as mentioned, in embodiments,since multiple discrete instances of TC Profile instances may coexistwithin a Platform 1000, where, in a few non-exhaustive examples amongmany, each may be germane to one or more different topics and/or may begermane to one or more characteristics or conditions or contextsassociated with the subject entity, any such discrete instances may betransiently or permanently spawned in response to any or all theconditions and user-based drivers as in the foregoing and describedthroughout any of the functionality within a DTS Platform.

Moreover, as mentioned, since TC Profiles may also and/or insteadcontain multiple topics which may, as well, in variations, coexist withsingle separately embodied instances, such multi-topic TopicalCapability Profiles may be deconstructed transiently or permanently andat any time, as in the foregoing. Such combinations are consistent withthe combinatorial mutability referenced earlier and throughout examplesand descriptions of the functionality provided within a DTS Platform,and any or all such transformative variations listed in thoseconnections may apply here, as well as others not listed but. Further,as before, any or all such transformative variations and mutationalfunctionality also may apply to any aspect of RAMS.

Note also, that any or all of the previously described DTS-based (and/orDTS-enabled and/or DTS-accessible) content-sensitive andcontent-responsive methods and procedures and interfaces that may beused in any aspect of the construction, permutation and maintenance ofany of the Assertional Simulation Models may also be used in any contextand in any combination within TCM but also to create, augment,supplement, operate upon, or otherwise manipulate or modify one or moreof the component structures within a DTS Topical Capability Profile (orone or more elements composed therein), where such methods andprocedures and interfaces that may be applied to any aspect of TCM andto TC Profiles may include but may not be limited to any or all of thoseenumerated in (or as may be inferred as applicable within) previouslypresented figures, descriptions and examples related to Reference DataModels (and other informatic entities), utilizing, for example, any orall content-sensitive and content-responsive methods and procedures andinterfaces associated with DTS semantic de-referencing capabilities aswell as any or all of the methods and procedures and interfaces that mayhave been described that incorporate, include, integrate and reconcilemixed ontology and hybrid-domain information from disparate sources aswell as from previously-created DTS Models and ancillary information. Inaddition, any or all of the operations cited above with respect to DTSTC Profiles may be additionally supplemented by any of the previouslycited looped, iterative and recursive and optimization capabilitieswithin or available to DTS where any or all such operations may utilizethe diverse technical capabilities previously cited, including but notlimited to data mining techniques, natural language processing,content-emergent and feature-mapping techniques as well as the plethoraof machine learning, evolutionary and AI-based technologies as may beemployed in these connections. Note as well, that such dynamic andadaptive construction and modification techniques may apply as well toany aspect of RAMS and its constituent elements.

Returning to the current non-limiting example, the sections within theTC Profile (the TCM Characteristics and TCM Properties hierarchies) aresimilar but compositionally distinct in that, in embodiments,topic-centric capabilities included in TCM Properties (which are, inDTS-terminology called capability properties in relation to the entityassociated with a TC Profile) are those things that may either qualifyor disqualify an entity from an association with one or more otherentities, for any reason. On the other hand, TCM Characteristics provideinformation that is itself neither qualifying nor disqualifying butwhich are descriptive or clarifying in nature and may provide additionalsuch ancillary information as, for example, identity-based,performance-related and supplementary functional information about thesubject entity's relationship or disposition to a topic but such that,in embodiments, any or all such ancillary information reposed within theTCM Characteristics hierarchy may also and/or instead be related to orderived from a topic in such a manner that this information may (inembodiments, optionally or conditionally) be used within as aconsideration (or variable) within a given associational mediationaction and/or which may be otherwise included as a modulatingconsideration within such a mediation which may (in embodiments,conditionally and optionally) be given some degree of relevance in agiven context, sequence or in combination with other properties andcharacteristics. Note, however, that, in general, the decision as towhether an item should be included within either the TCM Properties orTCM Characteristics sections may be made by platform operators and/or bysystem processes and/or by users but, in variations, may becontext-bound and/or content-related and, in any case, may bedynamically modified or changed at any time.

Finally, recall that in the earlier example involving an entity-type“user” having various capabilities to invoke or exert various actionsupon certain objects, this user had different capabilities depending onwhat condition they were in—in that example, this mutable capability wasshown as dependent upon which room they were in. This simple examplereinforces the DTS-wide property of the context-based (and inembodiments, content-responsive) mutability of information within aPlatform 1000. This mutability property is present within all theelements of the DTS Entity-Management Fabric, including in RAMS and inAPM and, in the current connection, in TCM. In TCM, this principle maybe seen in TC Profiles in that each entity may possess differentcapabilities depending on differences in “who they are”. Inimplementations, as stated in descriptions of APM operations referencedin the foregoing, entity-types are identically unique but may havedifferent states, or in DTS-terminology, different “roles”. The DTSusage of the term “role” is somewhat different from other usages inthese and related contexts within DTS: the comprehensive system-baseddefinition of that entity is anchored to and associated with the rolethat entity has currently assumed. In DTS-terminology thischaracteristic is called a DTS Capability Role, a moniker adopted toavoid confusion with the associations implied by usage of the term inother computational contexts and in colloquial connections. Note,however, that in embodiments, another modality of entity typing mayexist, namely that there is an additional quality that may attach to anentity called “variety”, In these implementations, there are differencesbetween an instance of an entity-type assuming a different DTSCapability Role and an entity-type being of a different variety. Thoughin some ways, the differences are in degree but more substantialdisparities manifest in the manner that associated information may bemanaged within the DTS Entity-Management Fabric and by APM, RAMS and TCMand other DTS and DTS-based computational components (such as, fromexample, the In-App Activity and Metrics capabilities). Thus, aninstance of entity-type “user” may be of variety 5 and in this varietymay have 6 distinct Capability Roles but the same instance ofentity-type “user” may also be of variety 3 and may have 2 CapabilityRoles. Note, however, that while entity-type varieties are optionalextensions within implementations (whereas the requirements associatedwith the binding of DTS Capability Roles to instances of entity-typesmay not be), in practice, these restrictions are transparent since a DTSembodiment may permit any of its entity-types to have only one varietyand may permit one DTS Capability Role for such instance. Note finally,that as with other aspects within TCM and the TC Profile, suchconfigurations may, in embodiments, be modified and controlled andmaintained by platform operators and/or by system processes and/or byusers but, in variations, may be context-bound and/or content-relatedand, in any case, may be dynamically modified or changed at any time.

In all cases, an entity may exist as variety of a given entity-type (ifthis quality is present in the subject DTS embodiment) but must be in aDTS Capability Role at all times and may exist in one and only one atany given time, noting that, in implementations, entities may have a“primary” or default Capability Role. This is shown in FIG. 22 and otherfigures and described in the context of RAMS but also in relation to theownership and inheritance properties executed by and managed by APM andthe various modalities of Assertional Simulation, but, as stated, theseand other characteristics apply equally within TCM (and in other areaswithin a DTS Platform, as well).

Thus, the entire edifice and skein of information related to an entity,as enumerated throughout examples and descriptions of DTS andAssertional Simulation, DTS System and Topical capabilities ownershipand activity history is tied to a specific role. As that entity changesto or assumes a new role, it remains the same instance of that entitybut the entire complex of information and functionality associated withthe entity in the previous may change, including but not limited to: i)the entire range of ancillary information associated with that entity;ii) the APM-managed identity, ownership, inter and intra-entityaggregation information as well as other identity-centric andidentity-related information; iii) RAMS-mediated System Capabilities;iv) TCM-mediated Topical Capabilities; v) In-App Activity Trackinginformation. In embodiments, any or all of that information may alsopermute and be passed into association with that entity within anewly-assumed (or newly-spawned) role and, in embodiments, all suchinformation may be modulated or modified or supplemented through directuser intervention and/or DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces.

Note as well that one or more elements of the information associatedwith a Capability Role associated with an entity may change when joiningan aggregation of entities (as in the associational proprieties executedin RAMS) but may also change (or be forced to changed) when any aspectof any such aggregation changes or is modified where such changes mayinclude (but may not be limited to) changes in membership compositionand/or changes in any or all of the role-associated ancillaryinformation tied to a member of that aggregation and/or changes to anyaspect of the RAMS-mediated and/or TCM-mediated associational rules thatpermitted any member of the aggregation to participate. Further,entities may assume Capability Role(s) for extended periods and/or maypass transiently through Capability Role(s), and Capability Role(s)themselves may, in embodiments, be transiently spawned in the context ofone or more operations executed within DTS. Moreover, in the samefashion, as discussed in connection with RAMS mediation, any or allinformation associated with a Capability Role may be transiently,temporarily, permanently or conditionally changed or modulated in anyway in any context, depending on factors present throughout a DTSPlatform 1000, including but not limited to factors based upon orderived from the presence of one or more other Capability Roles (orcombination of Capability Roles) that are associated with an entity (orassociated with that entity within an aggregation). As may be seen,there are many such combinatorial and context-responsive mutations andpermutations of any aspect within any component of entity-associatedancillary information, and to the degree that any variation is notenumerated here, the generalized combinatorial principles that motivatethis characteristic (which are, as mentioned, present throughout DTScomponents) accommodate such variants.

These changes are managed by the combined functionality within DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces within or associated with the DTS Entity-Management Fabric(as described in the foregoing) and within the content-sensitive andcontent-responsive methods and procedures and interfaces within itscomponents, APM, RAMS and TCM and associated operational aspects of aDTS Platform. Indeed, the high degree of fungibility characteristics ofentity-associated roles and the mutational-role and context-centricnature of the variegated information associated with an entity isanother example of the adaptive flexibility that suffuses a DTSPlatform, present, in embodiments, throughout its constituent andaffiliated computational components, methods and procedures andinterfaces.

As a concrete non-limiting example of how TCM and the its describedcharacteristics may be used, recall the earlier example of buyers andsellers in an exchange. In a variation of this example use-case whichemployed an exemplary but non-exhaustive embodiment of a DTS Platform1000 and cited the use of various modalities of Assertional Simulationand associated capabilities to assist in the management of acounter-party transactions, let an instantiation of a DTS Platform 1000be operated as an exchange facilitating financial transactions,specifically, facilitating loan transactions. In this simplifiednon-limiting example, assume that there are a variety of lender types(that is, a number of Capability Roles for entity-type “user”), bothinstitutional and private, which may offer numerous loan types to avariety of borrowers from different lines of business such that theproposed (and actual) transactions reflect a variety of types andstructures of the loans. Note also that the previously referenced“seekers” are those that wish to obtain a loan and the “providers” arethose who wish to provide the loans. But in this example, let a thirdplayer be present, a broker who, in general, may represent the borrowerbut may, in some cases, instead may represent the lender. Finally, aminor set of players in this example exchange are called ServiceProviders who may provide deliverables to any of the other parties suchas accounting and audit services.

Note here that the “topic” is financial instruments, and, as in thepreviously presented marketplace example, each seller possesses arepresentation of themselves, in this case treated as a Reference DataModel which embodies the tangible-within-the-platform representation ofthe lender and, in this example, substantially consists at least of theDTS Topical Capability Profile (TC Profile) for that borrower, lender,broker or other service provider. In this part of this example, appliedto other parts, as well, let this Reference Data Model be associatedwith a system user represented in DTS as entity-type “user” who hasassumed the Capability Role lender. Let this lender be called Lender 1.Further, let this Reference Data Model be called the Lender 1 Profile,an entity-type “data” that represents the entity-type “user”, in thiscase, Lender 1, in the DTS Capability Role “lender”. But note that thereare there are two entities here, entity-type “user” and entity-type“data”. In this example, Lender 1 (entity-type “user” in Capability Rolelender) spawns the Lender 1 Profile (entity-type “data”) and thus, inthis instance, Lender 1 (entity-type “user” in Capability Role lender)owns the Lender 1 Profile (entity-type “data”). Therefore, based uponthe APM-managed ownership schema in DTS, Lender 1 Profile (entity-type“data”) inherits all the entity-associated information (as detailed inthe foregoing and exemplified in FIG. 22) of Lender 1 (entity-type“user” in Capability Role lender), including all the previouslydescribed ancillary information, including but not limited to RAMS, TCMand APM and other associated information.

As an exemplar of this type of Reference Data Model (that is, aninstance of a variety of a Reference Data Model that subsumes (at least)an entity's Topical Capability Profile (TC Profile)), note that, invariations, such TC Profiles contain, in addition to the aforementioned,another collection of information (cited in FIG. 22 within block 10709-AOther Info User 1). In the present example, this potentially variegatedcollection of information may include such things as biographical data,location information, personal information from outside the system, someof which may be curated by the user (entity-type “user” in whateverCapability Role they happen to be in) but some of which may besupplemented and/or enhanced by other entities inside and outside thePlatform 1000. In the current example, a Lender Profile (the ReferenceData Model associated with that user) may contain such information butadditional information, as well, such as may be provided by one or moreother users in the system, other lenders, brokers or service providers,for example. Note, however, that some such informatic elements may ormay not be visible to some or all of these users such as those that mayfall within the same general category as biographical info andaddresses, but that others may not be visible at all, while may alwaysbe visible. The ability to access and view such information and tocontrol these abilities is a function of both RAMS and APM and isimputed and thus under the control, through APM, of the owner of entitywithin which such information may be reposed. Thus, in embodiments,examples of non-visible, non-accessible elements that may be subsumed insome such aggregates may include (but may not be limited to) one or moresystem-generated metrics related to this user and/or to other users inrelation to this user and/or to other activity and associational of thePlatform 1000, metrics that may be provided by and/or may have beenderived from and/or which may be otherwise obtained by means of and/orin conjunction with one or more of the content-sensitive andcontent-responsive methods and procedures and interfaces previouslydescribed in relation to In-App Activity functionality and other suchmeasurement and monitoring capabilities within a Platform 1000. While,as described, one or more elements of these capabilities may lie outsidethe user's control or purview and may not be accessible to the lender,other elements may indeed fall under direct user control, especially, invariations, one or more elements related to the user's SystemCapabilities (RAMS elements) and in such instances, this control may bepresented and managed through a series of interactive user-facinginterfaces.

In the current example, the Lender Profile for Lender 1 also containsthe DTS Topical Capability Profile (TC Profile) for that user in hisCapability Role as lender, as described in the foregoing. The TC Profileis used within this simplified example exchange implementation in avariety of ways such as, for example, as a useful information source forother users participating in the various party-counterparty transactionsthat may be invoked in these environments and may not only provideuseful explicit information but may also facilitate and enable othercapabilities provisioned with a DTS Platform 1000 in such modalities.Thus, as described, a DTS Topical Capability Profile (TC Profile) iscomprised of both the two main profiling elements: Topical MediationControl Properties (TCM Properties) as well as Topical Mediation ControlCharacteristics (TCM Characteristics), in this example configured ashierarchies as well as other auxiliary information, as described in theforegoing.

In embodiments, the elements that compose these hierarchies may, asdescribed, be initialized and maintained by platform operators and/or bysystem processes and/or by users based upon one or more definingschemata and other definitional mechanisms, but the assignment of anycapabilities to a specific entity is, in general (but not in all cases)executed by the owner of the subject entity, which may be either theentity itself (“self-owned”) or by one or more of the entities in theownership skein within which the subject entity exists. In this example,let Lender 1 be self-owned and is thus free to populate his TCMProperties hierarchy. The lender may employ one or more interactive userinterfaces to select one or more appropriate elements from thishierarchy, to define, in this instance, what type of lender they are—or,in the previously described terminology, “who they are”. In thisinstance, there are two main lender types configured as the second levelin the TCM Properties hierarchy tree in level 2, Bank and PrivateIndividual. In this non-limiting case, the platform operator hasdetermined (and defined in the governing schema) each of these lendertypes Capability Role(s). Thus, the Lender 1 may select either of theseproperties (or both) as Capability Roles. Assume in this example, thatLender 1 works for a commercial bank and thus selects the property“bank” and, in the next level, the property “commercial”. In some cases,the lender may be presented with many more choices and may select a widevariety of other properties but in interest of brevity, assume that thiscomprises a full explication for the properties of this lender defining“what this lender is”.

The next element in the TCM Properties aspect of the TC Profile is toaddress “what can this entity in this Capability Role do”? In this case,though in some instances, there may be a variety of loans types acommercial bank may make, assume that Lender 1 selects just one loantype, the property “debt” (under bank/commercial) and within the debtproperty, selects the child properties “Line of Credit” and “Term Loan”.Next, the lender selects child properties of Line of Credit, “Physical,Collateralized” and “Personal Guaranteed” and child properties of TermLoan, “Physical, Collateralized” and “Personal Guaranteed”. Note that,as described, though the child properties of “Line of Credit” and “TermLoan” share the same moniker, they are distinct properties by virtue oftheir parentage. Since these properties are representations ofactivities in which a commercial bank may participate, these propertiesaddress the question posed previously, “given what I am, what might Ido”?

In practice, this process may continue to produce a detailed set ofproperties for each DTS Capability Role, where the nominal lexical andgrammatical representations and the granularity and precision of theproperties are determined by the schemata created and managed byplatform operators and/or by system processes and/or by users (asdescribed previously), ontological definitions now de-referenced fromtheir original reference frames and recast in the now-DTS-defined frameof reference embracing this topic (in this case, financialtransactions). This de-referencing may be seen in many facets of thecomposition of the TCM Properties hierarchy but most generally, and inmanner that is largely pre-emptive of other considerations, any or allof the terms within the tree have different specific meanings indifferent organizations, in different jurisdictions and in differentcontexts. While, in general, such a term as “line of credit” may have anindustry-standard meaning, it is not necessarily universally accepted inits full semantical and relational depth in all relevant sectors and inall organizations, even within the same jurisdiction. Thus, theDTS-based (and/or DTS-enabled and/or DTS-accessible) content-sensitiveand content-responsive methods and procedures and interfaces executed byboth RAMS and by Topical Mediation Control (TCM processes that definethe semantical characteristics of topics and the representations ofvarious aspects of these topics) may deploy any or all of the previouslydescribed DTS semantical re-referencing functionality, as described, andthis is another novel use of this DTS capability.

Lender 1 may now augment the properties within the TCM Propertieshierarchy with information within the related Topical Mediation ControlCharacteristics (TCM Characteristics) hierarchy, as mentioned. In thiscase, let the lender select a top-level characteristic “Services” andtwo child nodes, “Insurance” and “Local Relationship Management”. Noteagain, that these are not properties in that they do not themselvesprovide a determination as to whether the lender may associate withanother entity but rather are supplemental aspects that assist inoptimizing such associations. These characteristics contribute to thequestion “what might I do” but are directed slightly differently in thatthey address the question “what things might I be able to add to what Ihave stated I might do that enhance those first set of things”, where,again, such enhancements are not determinative but supplemental to theassociational mediation executed by TCM.

Finally, in this instance the lender must answer the question “givenwhat I am and given both what I can might do and what I might do toenhance those things I might do, what might I do those things upon”?Once again, this is a domain-defined question, but in this instance,given that the domain and frame of reference created in the course ofconstructing these hierarchies, as previously described, in thisexample, there is third hierarchy defining the objects upon which theactivities are directed—in this case, who the businesses lender may wishto make loans to. This hierarchy may be constructed and maintained as inthe previous hierarchies and is here called the Business Type hierarchy.This can be an extremely detailed structure as may be seen in thewidely-accepted Standard industrial Classification Codes Systems (SICCodes), a hierarchy listing the many types and sub-types of businessesin many industries. Thus, the lender may select any number of businesseswith which they would like to do business from this list. Note again,however, that not only are SIC Codes from a different domain than thatoccupied by a financial services institution, since SIC Codes are usedacross the business world and not only by banks, but DTS semanticde-referencing may be applied to coopt the text strings and todisconnect the meaning and recast the semantical frame within the samethat encompasses the other elements within the DTS Topical CapabilityProfile.

This example of a DTS Topical Capability Profile is not finished beingprocessed in this case since there is no guarantee that the terms thatreference capabilities within the hierarchies are compatible. In thecurrent example, the rules and regulations that govern the activities ofthe users within this embodiment of a DTS Platform configured as aloan-oriented financial services exchange are highly complex andchangeable with the many nuances of the applicability of laws andregulations which may be subject not only to interpretation but may bejurisdictionally defined. Moreover, such jurisdictions may havestriations wherein different administrative levels exert authoritydifferently and with different rules over different subject matters.Thus, in the US, for example, certain laws are promulgated by theFederal Government and supersede State laws, while others are left tothe States, but in such conditions, some States may permit localjurisdictions to exercise control while others do not, and in any case,any one of those outcomes may differ across local jurisdictions.

In embodiments, this problem is addressed by considering theuser-generated DTS Topical Capability Profile consisting in this case ofthe TCM Properties hierarchy, TCM Characteristics hierarchy and BusinessType hierarchy, as an “unconditioned profile”. In DTS-terminology, inthe present example, applicable to any similar application of thesecapabilities, this expression refers to the state of the elements withina DTS Topical Capability Profile as “unconditioned” by any relevant lawsand regulations. This moniker and the possible solutions that DTSPlatforms may apply also apply to other such aggregations, as well. Anunconditioned DTS Topical Capability Profile may not always be usedeffectively as an element in topical associational mediation since itmay permit associations that are prohibited by applicable laws or may beconditionally permitted, or in some embodiments, may be forbidden ordiscouraged by customs and norms (again, in some instances,conditionally), and in further cases, may be excluded by platform-basedpolicies. Moreover, users may also wish to prohibit certain associationsor may wish to impose conditions upon such associations for users andother entity-types they “own” such as employees operating within aPlatform 1000.

Thus, in embodiments, one or more or heuristic processes may be appliedto one or more components (and to one or more elements within suchcomponents) within such unconditioned DTS Topical Capability Profiles(and, in embodiments, to other and such aggregations) to harmonize andresolve potential conflicts, conflict that may be of any form and haveany etiology, as described in the foregoing. The DTS-based (and/orDTS-enabled and/or DTS-accessible) content-sensitive andcontent-responsive methods and procedures and interfaces within suchrules-applying structures produce a “compliant DTS Topical CapabilityProfile”, again, a DTS-engendered term that conveys that the elementswithin the DTS Topical Capability Profile(s) used in the TCM-based (andin some cases, RAMS-based) associational mediation comports with theplatform-defined understanding of permissible association. Examples ofthe manner in which compliant DTS Topical Capability Profiles may bederived include (but are not limited to) one or more of the following:rules-based AI routines, machine learning techniques wherein elementswithin computational blocks may be trained to learn and execute rulesand hybrid systems that may combine any or all of the preceding methods.

In embodiments, there are many ways to implement such a collection ofheuristic processes, but the core intention in TCM-based mediation isnot necessarily to create an artificial or automated “complianceofficer” (for any industry), though in embodiments, such capability maywell be implemented, either within or accessible to a DTS Platform butrather, to provide the system and users with compliant associationalfunctionality, not the ruling on the legality of completing a givenaction within the context of that association. To the extent that suchcompliance mediation is unclear or conditional, in embodiments, one ormore methods and procedures and interfaces may present users withinteractive user interface components to resolve such issues. Forexample, if a certain association with a certain type of financialinstrument requires a local license (a real estate license, forexample), the user may be queried as to whether they have such alicense. In embodiments, since regulations may change with someregularity, platform operators and/or by system processes and/or byusers may maintain the rules within these methods through interactiveuser interface components, while in further refinements, changesmandated for certain rules may be signaled though one or more DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces that may monitor and deliver such changes, again, invariations, with user assistance or automatically.

Another example method that may, in embodiments, be used to create orderive a compliant DTS Topical Capability Profile (presented withoutforeclosing other methods and variations that may be possible) is toinvoke modalities of Assertion Simulation wherein, in the context of thepresent example, each of the hierarchies within the DTS TopicalCapability Profile (the TCM Properties hierarchy, the TCMCharacteristics hierarchy and the Business Type hierarchy) may betreated singly or combined into a single structure and designated as aReference Data Model. Then, as one example among many possibilities, oneor more “rules template” hierarchies may be embodied within a collectionof Assertion Models, embedding such example properties as “Prohibited”or Allowed” or “Prohibited with conditions” (where the conditions may beenumerated in descendant levels) or “Uncertain, check with User” and soon. The apportionment sub-Model may then be instantiated but with onlybinary values. Thus, in a manner similar to that used to allocateindirect expenses to different profit centers, as described in theforegoing, a user and/or system process may, for example, instantiatethe applicable rules by applying the binary values to the nodes in theReference Data Model. In embodiments, the Assertion Models may be madeto correspond to the rules for each administrative level within ajurisdiction, for example, or be made to comport with regionalvariations wherein customs and traditions and interpretations relativeto a topic may differ. Thus, one or more Reference Data Models may besubject to one or more Assertional Simulations, arranged in one exampleconfiguration, in a cascade manner wherein the Outcome Model from onestage of Assertional Simulations provides the Reference Data Model forthe next stage. Thus, in the context of the current example, theAssertion Model for the Federal laws and regulations would be coercedupon the Reference Data Model(s), producing Outcome Model 1. Then theAssertion Models for each State would be individually applied to OutcomeModel 1, invoking 50 instances of Assertional Simulation upon OutcomeModel 1, an operation that would result in the 50 Outcome Models. Thisprocess may be repeated until all the administrative levels within ajurisdiction have a unique Outcome Model. This collection embodies acompliant DTS Topical Capability Profile as it applies for a given topic(in this case, financial services).

Note, however, that, in embodiments, this process may be executed eitherbefore or after a user instantiates their profile, but in general, itmay be more efficient to do such eliding Assertional Simulations beforecompletion and to maintain the collection of a potentially larger numberof Outcome Models from which users may select their properties andcharacteristics. This selection sequence permits, for example, thesystem to disallow the user picking conflicting properties, such as, inthe present financial services example, certain activities that certaininstitutions may not execute. Note also that, in implementations, one ormore heuristic, and/or machine learning or/and hybrid methods previouslycited may also be used in any part of this sequence, and other DTS-based(and/or DTS-enabled and/or DTS-accessible) methods and procedures andinterfaces apart from or in conjunction with Assertional Simulation.

Continuing in the present example, the other users in the DTSimplementation of the loan-centric exchange, the borrowers and brokers,may also instantiate their versions of their DTS Topical CapabilityProfile but from a different perspective. For borrowers, after stating“who and what I am” (in this instance using, for example, the BusinessType hierarchy), the next question is not “what might I do” but rather,“what would I like to do and how would I like to do it”. This differencereflects the relative positions of lenders and borrowers in theparty-counter-party arrangement. Thus, once the borrower makes theirchoice, in embodiments, from borrower-centric versions of thehierarchies within a compliant DTS Topical Capability Profile, thisinformation may be incorporated within a “borrow loan proposal”, ahybrid multi-domain proposal that may include such items as past andpresent financial statement and records, projections and budgetaryproposals, text and graphical explanations and descriptions of thebusiness and so on. Note that each of these documents embodies amulti-domain aggregation with hybrid ontologies and multiple frames ofreference, as in the case with many Reference Data Models, and as withthe lender profile, any or all of the DTS-based (and/or DTS-enabledand/or DTS-accessible) content-sensitive and content-responsive methodsand procedures and interfaces (including those that may be used in DTSsemantic de-referencing may be employed here. In this example, thisproposal then becomes the DTS Topical Capability Profile for theborrower, and like that of the lender, may now be treated as ReferenceData Model.

Assume that there are many lenders of many varieties, each with adifferent DTS Topical Capability Profile and that there are manyborrowers who likewise have many DTS Topical Capability Profiles,wherein the former case, the TC Profile reflects what each lender can(or will) do (among other things) and in the latter case what businessthe borrower is in and what they are seeking. In implementations,depending upon the individual RAMS setting applied by the owner of eachTC Profile, each side in the counter-party transaction may then view theother. If a party decides it would like to engage with a counter party,the TCM mediation is applied to each respective TC Profile to ascertainwhether the two parties possess the shared capability to engage. Thereare many methods to accomplish this mediation, as described inconnection with RAMS, including invocation of one or more correlation orprobability-based calculations, but, in embodiments, other solutions maybe used, including application of appropriate transforms to convert thevarious DTS Topical Capability Profile into other format and/or domains,and execute domain-specific computations such as may be found, forexample, in computational geometry. Note, however, that for every role,there may be a different DTS Topical Capability Profile, and thus, adifferent set of mediations. If, for example, a lender chooses toaggregate with a group of other lenders, a collective DTS TopicalCapability Profile is compiled for this aggregation but each lender hasnow assumed a different role within their lender identity.

Recalling the earlier example of the simple buyer-seller exchange, ifthe TCM mediation permits the two parties to associate, each party maythen construct their own set of Assertion-Apportionment pairs and mayinvoke Assertional Simulation upon the respective Reference Data Models.In the case of lenders, they engage, in DTS-terminology, in Underwritingwherein each lender applies one or more Assertion-Apportionment pairs tothe Reference Data Models (the loan proposals, in this example) of eacheligible-by-mediation borrower (where the eligibility is theassociational capability as mediated through the previously-describedTCM processes). In the case of borrowers, as in the earlier example of abuyer-seller exchange, these users engage in a modality of AssertionalSimulation called “Ranking”, a DTS-engendered term referenced previouslywherein a borrower evaluates eligible-by-mediation lenders. In bothcases, the Outcome Models resulting from the Underwriting and Rankingexecuted by lenders and borrowers, respectively may be arranged in anumber of ordered lists based upon any number of criteria.

Note that, in embodiments, there may be many optimizations andimprovements to these processes and the use of DTS Topical MediationControl (TCM) may be applied in different ways to different topics, insome implementations, within the same instantiation of a DTS Platform.In one instance, extending the present example, borrowers and lendersmay both be queried during their parametrization of their DTS TopicalCapability Profile as to their preferences. Such evaluations may, inembodiments be multi-dimensional, eliciting opinions from users, forexample, reflecting how much a thing is liked and with what intensity.In addition, using one or more DTS-based (and/or DTS-enabled and/orDTS-accessible) methods and procedures and interfaces that may besubsumed within or associated with the aforementioned In-App Activitymetrics functionality, a user's actual behaviors may be correlated withtheir expressed preferences. These capabilities and the resultant datapoints may yield useful information in both the Ranking and Underwritingexercises of Assertional Simulation that borrowers and lender invoke,respectively, contributing to accrued Shannon entropic richness withinthe system, information that may be used by numerous DTS-based (and/orDTS-enabled and/or DTS-accessible) methods and procedures and interfacesthat may execute analytic and pattern mapping and recognitionoperations.

In embodiments, the methods and systems of Topical Capability Mediation(TCM) described herein associated with a DTS platform may be applied toa transaction management environment that may be embodied using, amongother things marketplace concepts and the like. DTS platformfunctionalities, such as Assertional Simulation and its relatedfunctions (e.g., de-referencing, and the like), RAMS and others may beconfigured for facilitating many aspects of transaction lifecyclesincluding, without limitation: pre-stage prospecting starting with, forexample a large number of possible prospects from various sources thatare reduced to a manageable number of targets that are then optimizedfor pursuit; drafting and/or generation of a proposal/profile to acounterparty or counterparties in a transaction; negotiating terms for adeal with a view of agreeing and executing, for example a letter ofintent; and other transaction related activities including: transactionstructuring, participant selection, access mediation, executionimprovement, and the like. Some of these and other DTS platformcapabilities will now be described in embodiments that facilitatetransactions. One such functionality is the use of RAMS to, among otherthings, mediate entity access to and execution of a transaction.

RAMS includes, among other things, a mediation capability thatfacilitates managing access control by entities, which may include usersin any number of roles, processes, and data, to other entities. Suchmediation and related capabilities may be referred herein as RAMStransaction functionality and the like. Access mediation capabilities ofRAMS may involve determining synergies for transactions among entitiesacross a plurality of dimensions of data associated with and/ordescriptive of entities which are generally representative of entitycapabilities, entity preferences, and entity history. While notexclusively limited to user-type entities, users can appear in the DTSplatform as many distinct entity-specific roles; each such role may beassociated with its own set of capabilities that may form a subset ofcapabilities of the entity assuming the role. Alternatively described,an entity may have capabilities for each of a plurality of roles thatthe entity can put forth for the purposes of, for example, performingtransactions and the like. Further to this concept, a user may seek toparticipant in transactions as a buyer, while that same user may seek toparticipate in other transactions as a seller. The reader may recognizethis as being common in business; a company buys products, such as via awholesale buyer role and sells those products, such as via a retailseller role. While the capabilities of this buyer and seller are rootedin the same company (e.g., embodied as an entity root certificate andthe like), capabilities relevant to a buyer role may or may not havelittle if any relevance to that entity in a seller role. Theinfrastructure and capabilities of RAMS facilitates perform mediationand other functions with this role-specific access distinction whilealso ensuring that activities associated with each of these entity-rolesis properly attributed to the root entity (e.g., again by updating, forexample, a history portion of a root certificate for the entity assumingthe role). In an example of entity-role handling by RAMS, a user mayengage with another user to perform a transaction (e.g., co-brokers of aloan). This engagement may be depicted in RAMS as an aggregation of thetwo users, with each individual user retaining his root entitycapabilities and the aggregation also retaining its aggregatedcapabilities (e.g., an aggregation certificate), yet the activitiesrelated to the aggregated entity can be linked to the individual user'sentities for purposes of mediation and the like as described herein.

While a general case of entity capabilities is exemplarily describedherein as entity-expressed capabilities (e.g., an entity, such as auser, can configure capabilities as the user desires). For at least atransaction-focused application of the DTS platform, entity capabilitiesare further refined programmatically, optionally using the methods andsystems of Assertional Simulation of the DTS platform. An application ofthe DTS platform for transactions (herein and elsewhere referred to asDTS-Transaction) considers jurisdictional factors that may directlyinfluence self-expressed or otherwise generated entity capabilities. Asimple example, and one that expresses aspects of a real-world instanceof DTS-Transaction involves a lender looking to loan money to borrowers.While capabilities of the lender and borrowers may, through accessmediation, result in an access certificate that indicates a loan may beexecuted, jurisdictional factors are highly relevant to and can overridethat result. A borrower may seek to perform a loan agreement in aparticular jurisdiction (e.g., Norfolk County, Massachusetts, USA). Alender may wish to provide the loan in this agreement. Before thelender's capabilities can be correlated to the borrower's capabilities(and preferences, history and the like) via RAMS-transactions (e.g., viaaccess mediation), the jurisdiction constraints are to be applied.Simply, this may be thought of as a form of legal vetting of the lenderto practice in the target jurisdiction. Further in this example, lenderA has capabilities for engaging in loan agreements. To determine iflender A can access a loan application of any borrower in jurisdictionC, lender A's capabilities must be vetted against the legal factors fora lending to borrowers in jurisdiction C. Described now is an example ofusing functionalities of the DTS platform, Assertional Simulation forthis purpose.

Two basic prerequisites for performing Assertional Simulation includeavailability of a Reference Data Model (RDM) and at least an AssertionModel (optionally with Assertional Apportionment Models). In thisexample, lender A's capabilities may represent the RDM—a source of datawith values that can be cast upon in Assertional Simulation.Jurisdiction C's requirements for a lender to practice therein mayrepresent an Assertion Model—a description of requirements,expectations, conditions and the like that can be applied to source datathrough Assertional Simulation to produce a Candidate Outcome Model thatmay represent, among other things, a suitability of the lender to dobusiness in the jurisdiction. Therefore, Assertional Simulation isperformed by casting the jurisdiction Assertion Model over lender A'scapabilities. Specifics of such casting are omitted from this examplebecause it is described for other embodiments elsewhere herein. However,at least for DTS-Transactions, applying Assertional Simulation fordetermining a suitability for an entity to conduct transaction-relatedactivity in a jurisdiction involves applying Assertional Simulation at aplurality of levels in a hierarchy of jurisdictions, specifically whilemoving down through the hierarchy of jurisdictions from a higher levelparent jurisdiction (e.g., USA) to a lower level child jurisdiction(e.g., Massachusetts). While the target jurisdiction here may be NorfolkCounty, MA. USA, the hierarchy of jurisdictions has at the top, forexample the USA jurisdiction, followed below by a child representing theMassachusetts jurisdiction, which is followed below by further childrepresenting the Norfolk County jurisdiction. Such a hierarchy may beuseful in other ways that build on the concepts of ownership andinheritance present in the methods and systems of the DTS platformdescribed herein.

As described herein, transactions, engagements, and the like may have anowner or lead who may retain control of certain functions of thetransaction, engagement and the like. In the case of entity groups, alead or owner of the group may retain control over other entities in thegroup; however, such control only extends to the entity's role oractions as part of the group. Ownership does not extend to an individualentity's root certificate, only to a certificate that is derived fromthe root certificate for a specific event, action, and the like. In anexample, I am a broker who has arranged to attach to a string of lendersfor conducting loans. As a broker, I see many loan requests (aka seekerproposal) and I can control which lenders see them or portions of them;I can control what part of a loan proposal each lender sees. My actionof exposing some portion(s) of some portion(s) of the loan requests is aRAMS event for which a RAMS event certificate is generated, and ahistory for the participants in these exposing events is updated. Inthis way, the broker retains the lead or ownership of the lenders inevents for which he is the lead. This notion of ownership is highlyconditional and contextual. A broker may not literally own a lender butmay, for the covenant arranged between the broker and lender, the brokerappears as the owner in RAMS-based events including mediation and thelike that involve the broker and lender.

As a DTS-Transaction example of such ownership and inheritance,requirements for conducting transactions in the USA, or more likelyprohibitions related to conducting transactions in the USA willpropagate down to a lower child level, such as at the state level in thehierarchy because, for example, such a hierarchy expresses ownership bythe higher level (e.g., USA), within the context of laws for conductingtransactions, of states (e.g., Massachusetts) for the purposes ofpropagating constraints. Something that is prohibited at the USA levelof the hierarchy will be prohibited at the state level as well. Of note,each state, while potentially co-located at a common level in such ahierarchy may only be accessible by traversing up to the next higherlevel and down to the sibling state. Simply, this articulates thepractice of law in the USA that each state can have independent laws,but all states must comply with USA laws and the like.

Therefore, in this exemplary application, lender A's capabilities areapplied as a RDM in an instance of Assertional Simulation by casting anAssertion Model representing the constraints for conducting transactionsin the USA jurisdiction. A result of this instance of AssertionalSimulation, a Candidate Outcome Model, may represent the capabilities oflender A for conducting transactions in the USA. Further refinement maybe performed in a subsequent instance of Assertional Simulation in whichthe lender A-USA Candidate Outcome Model is applied as a RDM along withthe state of Massachusetts requirements for conducting business inMassachusetts applied as an Assertion Model. An outcome of thissubsequent instance of Assertional Simulation, called for conveniencelender A-USA-MA capabilities, may represent lender A's capabilities forconducting business in the US state of Massachusetts. This sequence maybe repeated for the county of Norfolk (and further for individual towns,territories, addresses, and the like) to ultimately produce a set ofcapabilities for lender A that can be mediated with borrower(s) in thetarget jurisdiction for access to conduct transactions, using forexample RAMS mediation.

Other embodiments of this process may be envisioned, such as usingAssertional Simulation and/or the methods of RAMS mediation to producean Assertion model for the specific jurisdiction, such as by combiningrequirements from all higher-level jurisdictions in the hierarchy. Anysuch capabilities narrowing process may be performed at a time ofborrower interest or may be performed independent thereof, such as toproduce a lender-jurisdiction-specific set of capabilities that can becached for use at the time of borrower interest, for example. However,keeping in mind a need for ensuring that only the currently applicablerequirements for each jurisdiction are applied, performing at least someportion of this process at the time of borrower interest may make sense.Yet further, an Assertion Model of jurisdiction transaction constraintsmay be configured for future constraints, such as when determining apotential impact on lenders of changing a jurisdiction's laws foroperating as a lender in the jurisdiction. As described elsewhereherein, each instance of Assertional Simulation, including all entities,inputs, outcomes and the like may be tracked and retained for futureaccess. Simply, this RAMS capability to capture and retain all relevantinformation, actions, outcomes and the like provides a benefitunavailable on existing simulation/modeling systems, which is that itincreases the Shannon entropy of the platform through contextuallyrelevant data generation, tracking and collection. Specific techniqueswithin RAMS are described herein for such data collection and tracking,including without limitation the use of record numbers (rec-no) thatfacilitate unique identification and allocation of activities, data, andthe like to individual entities.

In embodiments, DTS-Transaction capabilities may embody step-wiseapplication of Assertional Simulation, a form of piece by piececonvolution that is not statistical in a Bayesian sense; ratherAssertional Simulation (e.g., casting an assertion over source data) islike a convolution at least because it involves applying the entireAssertion Model to each node in the RDM—e.g., applying each jurisdictionconstraint to each element in the lender's capabilities RDM.

To finish the exemplary process of access mediation of lender Avis-a-vis borrower B's loan application in jurisdiction C, afinal/select iteration of the Candidate Outcome Model representinglender A's capabilities in Jurisdiction C can be mediated with, forexample the capabilities of borrower B to determine at least a portionof access rights/control of the entities. While capabilities processedin this way provide a portion of access rights/control of the entities,access control of lender A accessing borrower B's loan application, forexample may be further enhanced/refined/reinforced through RAMSmediation of lender and borrower preferences and history.

Taking this example one step deeper into a technological realm, aplurality of directed graphs representing lender A's jurisdictionC-specific capabilities may be collocated with a plurality of directedgraphs of borrower B's capabilities to determine points of intersection.Similar actions may be taken with graphs representing each entity'spreferences and history. The points of intersection of these collocatedgraphs may be used to inform a decision regarding lender A accessingborrower B's loan application, for example. In embodiments hash-basedapproaches such as use of Merkle trees may optionally and in certainembodiments be used instead of directed graphs.

In embodiments, an application of DTS-Transaction may include use of alarge array of jurisdiction and transaction-type requirements tofacilitate generating jurisdiction-specific entity capabilities using,for example Assertional Simulation and the like. One such example is adata set that includes more than 100,000 individual statements of legalconditions and constraints for conducting business in one or morejurisdictions (or in a hierarchy of jurisdiction as in the exampleabove). Each of these individual statements may represent entries in anAssertion Model that are cast via Assertional Simulation over lender A'scapabilities reference model to generate a Candidate Outcome Model thatcan effectively be applied to a next level down of jurisdictionalhierarchy.

A large array of jurisdiction requirements, such as that noted above,could include, for example all the things that banks, individuals,brokers and a whole range of legal entities are permitted and/orprohibited from doing for all variations of transaction (e.g., finance)vehicles. While such an array may cover a very large number ofcombinations of constraints, requirements, and the like for each type offinancial vehicle across a large number of jurisdictions, efficienciesare gained by the inheritance described above of parent constraints bychildren nodes. One exemplary way in which efficiencies can be achievedinvolves omitting casting constraints for a given jurisdiction if theparent for the jurisdiction includes a prohibition thereof. If a parent(e.g., U.S.A) prohibits lending to incarcerated criminals, then bydefault, such lending will be prohibited in all states of the U.S.A.;therefore, processing can be reduced when casting a State AssertionModel because the result of casting is known before the State AssertionModel is even applied, even if such a constraint that appears to be inconflict with the U.S.A. constraint is present in the State set ofconstraints.

While certain constraints can be coded in an Assertion Model ofjurisdiction-specific transaction requirements as yes/no, or rather asprohibited or not prohibited in a jurisdiction, some constraints remainconditional, where a secondary condition must be met for a conditional“yes” response to become a complete yes response. An Assertion Modelcreated from such a set of jurisdictional constraints may support alltypes of requirements, including yes/no absolute and yes-conditional andthe like. As an example, that is relevant for DTS-Finance, lending moneyfor a home improvement may be permitted in a given jurisdiction;however, a lender may be required to possess a valid license to do so inthe jurisdiction. Therefore, while a capabilities certificate for alender may indicate that the lender has such a capability, thecapability may need to be validated, such as at the time that theAssertional Simulation is executed. In practice, an interface may beused to gather information that is needed in such conditionalsituations. One such interface may be an API-based interface between theDTS platform and a government database of lender licenses. Another maybe a user interface through which a user can submit a scan of a license,a license number or the like. Another form of conditional constraint mayinclude, for example, a capability to appeal a yes/no condition.

While this example is from the perspective of a lender seeking to attachto a loan deal of a borrower in a specific jurisdiction, the roles couldbe similarly reversed using the same or comparable techniques. In anexample, a borrower may seek to make a loan agreement in a specificjurisdiction. Using the example above, borrower B may attach to a loanapplication, a resulting access certificate thereof may form the basisfor a RDM that may be processed with Assertional Simulation through thesame hierarchies as lender A's capabilities, effectively producing ajurisdiction specific set of capabilities for borrower B's loanapplication. Other entity-roles in loan transactions, such as a broker,banker, loan consolidator, and the like may have their entity-rolespecific capabilities processed similarly. At least one result could bea marketplace of jurisdiction-approved entities, such as lenders,borrowers, brokers, and the like (each represented by an accesscertificate that includes or makes available jurisdiction-specificcapabilities, preferences, and history of the entities) in which RAMStype mediation could be used to mediate access of borrowers to lenders,and the like.

While a successful mediation may result in lender A lending money injurisdiction C to borrower B, there may be many such attempts thatresult in failed mediation, such as lender A not being qualified topractice in jurisdiction C, or borrower B not having sufficiently highborrower quality (e.g., FICA score) to allow the borrower and lender toattach in a lending arrangement. Information that contributes to afailed mediation may provide valuable insight for the participatingentities, other entities, and the DTS-Transaction platform overall tomake improvements. Therefore, retaining and tracking such information,enabled by the RAMS capabilities of the DTS platform, can be used in amachine learning feedback improvement process that takes thisinformation and adjusts, for example how mediation handles capabilitiesdata, or suggests to a borrower aspects of a loan proposal (e.g.,application) to adjust in future applications, and the like. All aspectsof information and processes in a DTS platform may benefit from suchfeedback.

Information collected about mediation results, including informationthat does not correlate positively for two entities in a mediationprocess may also be presented in various interfaces to entity users andthe like, as well as to pattern detection algorithms and the like toprovide greater visibility and contextual value thereof.

DTS-Transaction approaches transaction mediation from a perspective thatallows the ever-increasing Shannon entropy of the data available to theplatform to facilitate determining and/or predicting, among other thingswinners and losers for transactions; not in the sense of good or baddeals, but in terms of, for example, successful attachment totransactions. This can be embodied as a range of metrics, examples ofwhich might include a count of successful transaction attachments (e.g.,mediation that results in entities being granted access to the resourcesof the transaction, and the like), rates of successful mediation, trendsof mediation results, and others. Further information related tomediations, including for example history of access-granted participantsin a transaction can be used as, for example, a yardstick for success.In this way, entities that practice strategies and techniques (e.g., asdetermined by activities in an entity history) consistent with entitiesthat have successful transaction mediations and the like may bestatistically more likely to successfully attach to transactions. Suchinformation may be used in methods and systems, some of which aredescribed herein and other that are known by those knowledgeable in thestate of the art that enable ranking lenders, borrowers, proposals,strategies, and the like. The activities surrounding other similar loansor activities, the strategies employed and the like are factors in thisunderwriting and ranking—example: a strategy is selective reveal—whathappens is a broker will reveal only a portion of a deal, another is Iwill expose that to fewer lenders.

Such notions of winners and/or success and the like that are driven by,among other things, measures of data generated in the platform, ratherthan by achieving arbitrary objectives may be implemented through graphtechniques that rely on experiential data to strength certain nodalconnections relative to others. As an example, a nodal connection mayexist for a capability of an entity. By applying measures of preferencesexpressed by the entity that are relevant for the capability in a formof weighting, paths in the graph may gain value over others. Similarly,by applying measures of history (e.g., actions taken by or inassociation with an entity) may similarly weight such nodal connections.If, for example a lender has a capability to close loans with pigfarmers, the lender's preferences with respect to such loans maystrengthen or differentiate this capability relative to othercapabilities (e.g., closing loans with sheep farmers) and relative toother lenders, and the like. Likewise, if the lender has a history richwith closing loans with pig farmers, the nodal connection representingthe lender's capability to close loans with pig farmers may bestrengthened by this history. Such ability to distinguish value ofinter-nodal connections in a graph further may lead to intra-graphanalysis (e.g., determination of which nodal connections represent acapability strength of an entity) and inter-graph analysis (e.g., whichlender of a plurality of lenders has the strongest nodal connection forpig farmer loan capabilities).

Recombinant mediation for access control may take advantage of certainproperties of entities, such properties characterizing access-relatedaspects of the entities. Access-related properties may includecapabilities (e.g., what can the entity do, such as perform a type ofreal-world transaction), preferences (e.g., what does the entity prefer,such as types of real-world transactions), and history (e.g., whatactions has the entity performed and what actions by others haveaffected the entity).

Access capabilities generally may be self-expressed by the entity (e.g.,I am registered to practice Law in Rhode Island, I teach 10th gradepublic school math, and the like). Such entity access capabilities mayconnect to or be accessed through a root certificate of the entity.Evidence of access capabilities (e.g., a current license to practicelaw) may be found in or via the entity root certificate. This evidencemay be used during forms of mediation, during Assertional Simulation orin external applications of the Platform (e.g., when evaluating aproposal for legal representation by an entity, an Assertion Model maybe configured to check for such evidence). In certain applications ofrecombinant mediation for access control, such as performingtransactions (e.g., financial transactions and the like), accesscapabilities may be generated through an instance of the AssertionalSimulation process that is described herein. Further description ofgenerated access capabilities is included with the explanation oftransaction support within DTS (DTS-Transaction).

Entity preferences may also be self-expressed. Such entity preferencesmay also be expressed with an intensity to further refine with accessmediation and other operations that benefit from attaching an intensityof a preference, such as when ranking entities against criteria and thelike. In embodiments, while an entity may prefer small business clientsover larger clients for certain transactions, the intensity of thispreference may be specified on an intensity scale. Not only can thispreference, but its intensity be used with mediation and otherDTS-platform functions and applications. As an example, the lawyer whoprefers small business clients a little more (e.g., with low intensity,say 2 out of 10) over larger clients may still rank highly by a largerclient evaluating lawyers. Whereas if another lawyer strongly preferssmall clients (e.g., with high intensity, say 9 out of 10) over largerclients, this other lawyer may not rank as highly by a larger clientevaluating lawyers.

In embodiments, techniques for RAMS mediation may include applicationsof graphs, such as directed graphs (e.g., acyclic graphs, and the like),to represent nodal links among, for example preferences, capabilitiesand the like of an entity. Links to or among preferences may beinfluenced by an intensity thereof, such as by weighting a linkcorrespondingly with the intensity of the linked preference.

RAMS mediation for access may further be based on entity activityhistory. Activities in which entities may engage, including use of DTSresources, processes, requests for access, mediation actions,Assertional Simulations, and the like are tracked and associated with,for example the root certificate of each participating entity. Activityhistory may be analyzed for trends and the like that may play into amediation of access for the entity. In an example, the entity with acapability to finance pig farmers may have a history that includes allactivity of the entity related to financing pig farmers. This historycan be analyzed to determine, for example, a count of pig farmerfinancing transactions for the entity. In this example, the pig farmerfinancing history can inform mediation functions as to the relevance ofthe claimed capability, so that experienced pig farmer financiers couldbe distinguished from novice financiers, and the like. The recombinantaccess mediation system (RAMS) methods, systems, and infrastructuredescribed herein facilitates such activity collection, tracking, andanalysis through, for example RAMS system capabilities including,without limitation, Event Certificates and the like.

Embodiments of such mediation correlation may be based onrepresentational domain concepts that facilitate determining points ofintersection of data sets and the like. Each element of capabilities,preferences, and history to be correlated for mediation of accesscontrol may be represented as a shape determined at least in part bypresence and intensity of attributes of the element, such as presence ofa specific capability, intensity of a preference, and a history of theentity related to a domain, such as financial transactions and the like.The shape may be derived from a graph of entity capabilities,preferences, history and the like and may include directed graphs,acyclic graphs and the like. By collocating such graphs (e.g., assigningthem a common origin, and the like) and determining their intersections,mediation for access control may be granted based on theseintersections.

Recombinant mediation for access control may look for synergies amongpotential participants in a potential event for which mediation is beingperformed. In an example, two entities, call them entity 1 and entity 2may be interested in conducting a transaction in a specific subjectmatter domain. Capabilities, preferences, and history of the twoentities may be aligned to attempt to identify commonality or at leastcorrespondence of capabilities in the transaction domain. In a simpleexample, a directed graph of the capabilities of entity 1 may becollocated with a directed graph of the capabilities of entity 2. Notethe capabilities in these graphs may be a subset of each entity'scapabilities, such as a subset that pertains to the subject matterdomain of the transaction. These two directed graphs may further becollocated with a graph of capabilities of the transaction, such asterms of a deal. Mediation, based on points of intersection of thesegraphs may suggest that the capabilities of entity 1 correlate well withthose needed for the transaction, whereas the capabilities of entity 2do not. A result of mediation may be to deny access by entity 2 to thetransaction, or at least a portion thereof. Note that such a result ofmediation may be attached to each entity's activity graph, effectivelymarking that entity 2 did not sufficiently correlate with aspects of thetransaction to warrant granting access, and the like. As noted above,this information may be used in future mediations for access by entity2, where appropriate, to influence a suitability of entity 2 for accessto a subject entity/deal through mediation.

Mediation for access control may include collocating graphs frommultiple entities to determine intersections. As an example,capabilities graphs for two entities might be processed for intersectiondetermination when a request is made by one or both to form a group ofentities. Capabilities that correlate based on the determinedintersections may become the foundation for a set of capabilities forthe resulting group formed by the two entities. In embodiments,capabilities for a group of entities may include only those capabilitiesthat each entity in the group shares. A group or aggregation certificaterepresenting this set of capabilities might be formed as well for use infurther processing. Although this example refers to capabilities graphsfor two entities, in practice a capability graph for each type ofcapability of an entity (of which there may be many for an entity) maybe formed and used in this mediation. As noted, each capability,preference and corresponding intensity, and history may be configured asits own graph. In an example an entity's history of advertising productsand that same entity's history of selling the advertised product may bearranged as a graph that can be collocated for mediating access by theentity to, for example business deals with advertising agencies,distribution agreements with product distributors and the like.

In embodiments, the RAMS framework along with mediation techniques, suchas those described herein may form a foundational operating environmentfor an exchange (e.g., a marketplace) for requesting and managingentity-to-entity attachments and the like.

While reference is made to entities, such as users, as having a rootcertificate and capabilities, access capabilities, preferences, andhistory, may be configured into entity-role specific certificates tofacilitate mediation. A lender entity may, in one instance play the roleof lender. That same lender entity may take on a role of buyer forservices, such as underwriting, background checking and the like. Thissame entity may therefore be associated with two different accesscertificates. The relevant capabilities, preferences, and historyrelated to the buyer role may appear in the buyer role certificate,whereas the lender certificate may express lender-related capabilities,preferences, history and the like. In this way a single entity-specificroot certificate may retain all capabilities of the entity and may bemaintainable by the entity as capabilities change or are added.

As noted above, entities may attach to form groups of entities. Inexamples, lenders may form syndicates, brokers may co-broke deals,borrowers may form borrower cartels, and the like. Each such group maybe characterized by a group, or aggregated access certificate that mayreflect the common access capabilities, preferences, and history of itsentity-members.

Recombinant mediation for access control, as described herein, mayadditionally rely on activity history associated with an entity whenmediating access by or to the entity. As noted herein, activities aretracked and linked to an entity, such as to an activity certificateand/or activity portion of an entity root certificate and the like.Entity activities that may be tracked and/or included in a history ofactivities of an entity may include activities surrounding an event towhich the entity has attached (e.g., via mediation access as describedherein) or attempts to attach to or participate in. Mechanisms fortracking entity activities are described herein with respect to RAMS,and the like. Any or all such mechanisms, structures, functionality,data bases, and the like, including without limitation use of a uniquerecord number (herein rec-no) for each RAMS event may be employed forgenerating and managing entity history of activities. Activities thatmay contribute to mediation may include, for example use of platformservices, including services associated with but not directly operatedthrough the platform (e.g., analytic and other services). Platform andother applications or services may collect and generate in-app metricsthat may be a source of activity history for entity access mediation.Non-limiting examples of in-app metrics may include failed log-inattempts, activity level of the entity in and/or to the application, andthe like. Other activities may include strategies in the context ofAssertional Simulation used by, for example borrowers when seekingloans. Entity roles, such as being a borrower and the like may employstrategies within an application domain of the DTS platform, such asperforming transactions. Further discussion of mediation and operationsof the DTS platform for transactions (referred herein variably asDTS-Transaction or DTS-Finance) are described elsewhere and includeexamples of strategies of entity roles such as borrowers, lenders,brokers, and the like.

While specific measures of entity activity and metrics derived therefrommay contribute to an outcome of mediation of access for an entity,intangible aspects may also be factored into access mediation. Machinelearning and self-organizing functionality, such as fuzzy logic and thelike may be applied to apply an intangible aspect (e.g. a preference) tomediation, such as by identifying a likelihood of an entity acting basedon preference and the like.

Described herein, such as in context of RAMS, certificates thatfacilitate identifying events and activity related to access mediationare generated and saved for, among other things, facilitating trackingof such activity including, without limitation the entities involved. Anoutcome of access mediation may include attachment of two entities, suchas a user and a process (e.g., a transaction process and the like),which may trigger generation of a RAMS certificate, such as RAMS eventcertificate, RAMS instance certificate and the like. A unique, at leastwithin an instance of a DTS platform, record number or rec-no may beattributed to each RAMS certificate. All activities related to a RAMScertificate may be tracked and memorialized (e.g., for audit purposesand the like) by this rec-no. Information trackable by a rec-no may, forexample, identify an access certificate for a group of entities (anaggregated certificate) when such a group has attached with anotherentity in a RAMS event and the like. Each entity in the aggregated groupof entities may be further identified by that aggregated certificate.Therefore, activities tracked by an individual rec-no can be ascribed toroot certificates of participating entities, even when only theaggregated group (itself an entity (or at least a meta-entity) in theDTS platform) is granted access via a RAMS mediation process and thelike. Thus, entity history of an individual entity may be influenced byRAMS events of any group to which the entity has joined. Whileaggregating entities into a group is described here, any attachment, ofwhich aggregation is merely one example, of two or more entities isknown and managed by RAMS and therefore is subject to the sameassociating activities of the individual entities in the attachment tothe individual entity's history for, as an example, access mediation.

In an exemplary DTS-Transaction deployment of RAMS mediation, varioustypes of activities that may be tracked through a rec-no may include allactions associated with the event identified by the rec-no, includingin-app messages, changes to a proposed deal, lockdown (a specificDTS-transaction action described elsewhere herein) of the deal, and thelike may all be tracked and influence an entity's ability to attach tofuture deals.

In embodiments, unlike conventional competitive game theory in whichcompetitors are measured against some arbitrary objective (e.g., mostruns in a ball game, most points in football, surviving in a survivalgame, and the like), the DTS platform, and more specifically theDTS-Transaction application of the DTS platform, determines winnersbased on the content or measures thereof. As noted above, a lender maybe deemed to be successful (e.g., within the context of aDTS-Transaction marketplace, for example) in conducting transactionsthrough a range of measures, not merely if the transactions areprofitable. Indeed, such information (a real-world result of atransaction, such as a financial profit/loss result) may be outside thescope of DTS-Transaction. Nonetheless, winners in DTS-Transactions canbe determined without consideration of extraneous factors, such asprofit from a loan or default of a borrower because the data itself—datathat represents the actions of the entities provides rich informationthat can be measured and analyzed. In a simple example, a winner of acompetition for a loan may be deemed the lender chosen by the borrower.This lender is a winner of a zero-sum competition (only one lender ischosen); however, the techniques applied to determine this winner arefound at least in part to be non-zero-sum (ranking lenders wherein aranked scalar value may be assigned to each lender). Relative measuresof a range of transaction-related factors, such as a degree to which aset of lender capabilities (e.g., as a Reference Data Model) aligns withjurisdiction and borrower requirements (e.g., as an Assertion Model)through the application of Assertional Simulation may result in rankingof lenders by the borrower (non-zero-sum); however, from thisnon-zero-sum application, a final winner in a zero-sum competitionemerges (e.g., only one lender ultimately is chosen by the borrower).Note that even though this example indicates a single lender is chosen,even when multiple lenders collaborate on a loan, the result is stillzero-sum.

A DTS-platform may facilitate accessing data for detecting patterns andthe like that may not be accessible using state of the art deep learningtechniques applied to existing data-rich domains through themanipulation of such domains with capabilities such as de-referencing,Assertional Simulation and the like. Current technology pattern andlearning systems may be compromised in their ability to yield goodresults due to, for example, limitations placed on data by its sourceenvironment, such as by structural constraints (e.g., how data isorganized, how it is constrained to be related to other data and thelike). A DTS-platform may access de-referenced data through AssertionalSimulation and the like to facilitate finding and/or representingalternative solutions to pattern recognition in complex, data-richdomains. An inherent property of Assertional Simulation in aDTS-platform is a gain in Shannon entropy that Assertional Simulationproduces and that the DTS-platform capability RAMS and TCM facilitatesbeing captured, tracked and accessed. This increase in visibility ofinformation may facilitate, among other things, providing an environmentin which techniques like set theory (e.g., helical search, and the like)may be fruitfully applied. Additionally, the methods and systems ofAssertional Simulation and its associated components (e.g., ReferenceData Models, Assertion Models, Apportionment sub-Models, and the like)may produce knowledge in the form of Outcome Models and the like thatcan be used for such purposes as seeding Artificial Intelligence systemswith models based on reality (e.g., based on actual data representing areal-world situation, and the like). Such seeding of AI may be usefullyapplied in situations where, for example, deep learning fails to yieldgood results.

In embodiments, DTS entity chaining may be flexibly configured in anumber of geometric and notional configurations. In one variation amongmany, using methods and procedures composed within or accessible to theDTS entity chaining process, the connections between InstanceCertificates (or some other DTS objects) may be arranged but may not belimited to any of the following configurations: uni-directional,bi-directional, forward-backward looking, left-right looking and anycombination thereof, but may also be “stacked” (and thus upward-downwardlooking) where the criteria defining levels and directionality may becontext and/or content based and may, in variations be oriented basedupon entity-type. Other variations involve the nature of the tasks DTSenchainment protects since system processes are known at the time ofdeployment, pre-arranged process associational certificates may becreated and may be uniquely associated with certain other entity-types.In some variations, Instance Certificates may be considered nodes in agraph where the structure may be derived over a time and/or an eventline and wherein the strength of the connections between Certificatesmay become one factor (of many, in some variations) in calculating ormaintaining or contributing to the geometry of the chain. Othervariations include methods that may dynamically arrange InstanceCertificates (or other DTS objects) around one or more loci where eachlocus may be based upon one or more entity-types, where one result couldbe a type of distorted sphere that may be re-formed or deformed basedupon selection of the loci and upon other factors. In furthervariations, in such an entity-oriented, locus-based geometric system,the strength of the connection between nodes may also be indicative ofone measure of trust, thus expressing the nature of the trustedintermediary relationships in a geometric configuration, wherein, forexample, the most trusted elements are closest to the center, withlesser proceeding outward.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a facilityfor user creation and ownership of multiple simulation-derived OutcomeModels based upon per-user-modulated Assertion Models that are projectedacross shared reference data and per-user-modulated reference data. Inembodiments, a software platform for decision support is provided havingan Assertional Simulation Modeling and Simulation system using at leastone Reference Data Model and at least one of at least one AssertionModel and at least one Apportionment sub-Model to produce at least oneOutcome Model and having a system to manage ancillary parameters andexecutable logic associated with users, data models and data modelelements within a user-modulated, content-adaptive, context-adaptivemodeling and simulation system. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having permutationand cloning of models, nodes and nodal properties in user-modulatedAssertion Models projected upon Reference Data Models. In embodiments, asoftware platform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a collaborative, comparative and competitive environment foroutcome simulation models with adaptive user-modulated Assertion Modelsthat are projected upon Reference Data Models.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a content-adaptive andcontext-adaptive, role-based and object-based permission and accesssystem. In embodiments, a software platform for decision support isprovided having an Assertional Simulation Modeling and Simulation systemusing at least one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a system for time and eventprogression of Outcome Models based on adaptive, user-modulatedAssertion Models and Reference Data Models. In embodiments, a softwareplatform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a system for time and event progression of Outcome Models withmultiple modal periodicity based on adaptive, user-modulated AssertionModels and Reference Data Models. In embodiments, a software platformfor decision support is provided having an Assertional SimulationModeling and Simulation system using at least one Reference Data Modeland at least one of at least one Assertion Model and at least oneApportionment sub-Model to produce at least one Outcome Model and havinga system for forward and backward projection of Outcome Models composedwithin a time or event based sequence using adaptive, user-modulatedAssertion Models and Reference Data Models. In embodiments, a softwareplatform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving Cardinality designation within collaborative and competitivemodeling using user-modulated Assertion Models and reference data.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a system for application of analyticand pattern recognition techniques to model pools within auser-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having dynamicrevision of Assertion and Reference Data Models via content-adaptive andcontext-adaptive recursion in Assertional Simulation within auser-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having dynamicrevision of associated data structures via content-adaptive andcontext-adaptive recursion in Assertional Simulation within auser-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having dynamicrevision of definitional vocabulary structures in heterogeneousontologies within a user-modulated, content-adaptive, context-adaptivemodeling and simulation system. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a system foradaptive revision of nodal structures using Assertional Simulationacross reference data within a user-modulated, content-adaptive,context-adaptive modeling and simulation system.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a system for adaptive permutation ofrelational structures using Assertional Simulation across reference datawithin a user-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a userinterface in which a user can model taxonomic assertions or othermethods of data organization as applied to reference data foruser-selected time intervals. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a simulationsystem using machine learning for modeling competing opportunities thatdepend on taxonomic assertions (or other methods of data organization)as applied to reference data. A software platform having a system forproviding business simulation as a service. In embodiments, a softwareplatform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a data integration facility for automated extraction,transforming and loading reference data from an accounting softwaresystem.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a simulation system for modelingalternative income and expense structures across profits centers of anenterprise. In embodiments, a software platform for decision support isprovided having an Assertional Simulation Modeling and Simulation systemusing at least one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a simulation system that handlesnested, multi-level profit center models with user-selected timeintervals. In embodiments, a software platform for decision support isprovided having an Assertional Simulation Modeling and Simulation systemusing at least one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a simulation system in whichsimulation models are required to be composed as nested subsumptivecontainment sets. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a simulationsystem wherein simulation is not based on time. In embodiments, asoftware platform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a simulation system that models a proposed taxonomic assertion(or other methods of data organization) with respect to a reference dataset at more than one selected time or event interval. In embodiments, asoftware platform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a simulation system for modeling before and after effects of thecomposition and structure of a taxonomic assertion (or other methods ofdata organization) as applied to reference data for user-determined timeintervals.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a facility for user-modulated,content-adaptive, and context-adaptive model reorganization. Inembodiments, a software platform for decision support is provided havingan Assertional Simulation Modeling and Simulation system using at leastone Reference Data Model and at least one of at least one AssertionModel and at least one Apportionment sub-Model to produce at least oneOutcome Model and having a facility for user creation and ownership ofmultiple simulation-derived Outcome Models based upon per-user-modulatedAssertion Models that are projected across shared reference data andper-user-modulated reference data. In embodiments, a software platformfor decision support is provided having an Assertional SimulationModeling and Simulation system using at least one Reference Data Modeland at least one of at least one Assertion Model and at least oneApportionment sub-Model to produce at least one Outcome Model and havinga system to manage ancillary parameters and executable logic associatedwith users, data models and data model elements within a user-modulated,content-adaptive, context-adaptive modeling and simulation system. Inembodiments, a software platform for decision support is provided havingan Assertional Simulation Modeling and Simulation system using at leastone Reference Data Model and at least one of at least one AssertionModel and at least one Apportionment sub-Model to produce at least oneOutcome Model and having permutation and cloning of Models, nodes andnodal properties in user-modulated Assertion Models projected uponReference Data Models.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a collaborative, comparative andcompetitive environment for outcome simulation models with adaptiveuser-modulated Assertion Models that are projected upon Reference DataModels. In embodiments, a software platform for decision support isprovided having an Assertional Simulation Modeling and Simulation systemusing at least one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a content-adaptive andcontext-adaptive, role-based and object-based permission and accesssystem. In embodiments, a software platform for decision support isprovided having an Assertional Simulation Modeling and Simulation systemusing at least one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a system for time and eventprogression of Outcome Models based on adaptive, user-modulatedAssertion Models and Reference Data Models. In embodiments, a softwareplatform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a system for time and event progression of Outcome Models withmultiple modal periodicity based on adaptive, user-modulated AssertionModels and Reference Data Models. In embodiments, a software platformfor decision support is provided having an Assertional SimulationModeling and Simulation system using at least one Reference Data Modeland at least one of at least one Assertion Model and at least oneApportionment sub-Model to produce at least one Outcome Model and havinga system for forward and backward projection of Outcome Models composedwithin a time or event based sequence using adaptive, user-modulatedAssertion Models and Reference Data Models.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having Cardinality designation withincollaborative and competitive modeling using user-modulated AssertionModels and reference data. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a system forapplication of analytic and pattern recognition techniques to modelpools within a user-modulated, content-adaptive, context-adaptivemodeling and simulation system. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model having dynamic revisionof Assertional and Reference Data Models via content-adaptive andcontext-adaptive recursion in Assertional Simulation within auser-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having dynamicrevision of associated data structures via content-adaptive andcontext-adaptive recursion in Assertional Simulation within auser-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having dynamicrevision of definitional vocabulary structures in heterogeneousontologies within a user-modulated, content-adaptive, context-adaptivemodeling and simulation system.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a system for adaptive revision ofnodal structures using Assertional Simulation across reference datawithin a user-modulated, content-adaptive, context-adaptive modeling andsimulation system. In embodiments, a software platform for decisionsupport is provided having an Assertional Simulation Modeling andSimulation system using at least one Reference Data Model and at leastone of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a system foradaptive permutation of relational structures using AssertionalSimulation across reference data within a user-modulated,content-adaptive, context-adaptive modeling and simulation system. Inembodiments, a software platform for decision support is provided havingan Assertional Simulation Modeling and Simulation system using at leastone Reference Data Model and at least one of at least one AssertionModel and at least one Apportionment sub-Model to produce at least oneOutcome Model and having a user interface in which a user can modeltaxonomic assertions (or other methods of data organization) as appliedto reference data for user-selected time intervals. In embodiments, asoftware platform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a simulation system using machine learning for modeling competingopportunities that depend on taxonomic assertions (or other methods ofdata organization) as applied to reference data. In embodiments, asoftware platform for decision support is provided having an AssertionalSimulation Modeling and Simulation system using at least one ReferenceData Model and at least one of at least one Assertion Model and at leastone Apportionment sub-Model to produce at least one Outcome Model andhaving a system for providing business simulation as a service.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a data integration facility forautomated extraction, transforming and loading reference data from anaccounting software system. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a simulationsystem for modeling alternative income and expense structures acrossprofit centers of an enterprise. In embodiments, a software platform fordecision support is provided having an Assertional Simulation Modelingand Simulation system using at least one Reference Data Model and atleast one of at least one Assertion Model and at least one Apportionmentsub-Model to produce at least one Outcome Model and having a simulationsystem that handles nested, multi-level profit center models withuser-selected time intervals.

In embodiments, a software platform for decision support is providedhaving an Assertional Simulation Modeling and Simulation system using atleast one Reference Data Model and at least one of at least oneAssertion Model and at least one Apportionment sub-Model to produce atleast one Outcome Model and having a simulation system in whichsimulation models are required to be composed as nested subsumptivecontainment sets.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having”, as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be understandable by those skilled in the artthat many changes and modifications may be made thereunto withoutdeparting from the spirit and scope of the present disclosure asdescribed in the following claims. All patent applications and patents,both foreign and domestic, and all other publications referenced hereinare incorporated herein in their entireties to the full extent permittedby law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3G, EVDO, mesh, or other networks types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flowcharts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flowchart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one of ordinary skill tomake and use what is considered presently to be the best mode thereof,those of ordinary skill will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 1112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §1112(f).

Persons of ordinary skill in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

1. A computer program product comprising computer executable codeembodied in a non-transitory computer readable memory that, whenexecuting on one or more computing devices, performs the steps of:taking source data elements from a source of data stored in the memory,wherein content of the source data elements is defined by a sourceontology and according to a source geometry; extracting the content fora portion of the source data elements as content data elements;representing at least one source ontology definition for the portion ofthe source data elements, and at least one constraint imposed by thesource geometry for the portion of the source data elements, as semanticdata elements; and constructing a reference data model in the memory bycoercing a reference data model ontology that is independent of thesource ontology and independent of the source geometry upon the contentdata elements and the semantic data elements.
 2. The computer programproduct of claim 1, wherein coercing a reference data model ontologyincludes informatic convolution of elements of the reference data modelontology with the content data elements and the semantic data elements.3. The computer program product of claim 1, wherein constructing areference data model includes representing at least one ontology-derivedconstraint as one or more data elements in the reference data model. 4.The computer program product of claim 1, wherein constructing areference data model includes representing at least one geometry-derivedconstraint as one or more data elements in the reference data model. 5.The computer program product of claim 1, wherein constructing areference data model includes excluding from the reference data model ameaning of at least one source data element that is configured in thesource ontology for the source of data.
 6. The computer program productof claim 1, wherein constructing a reference data model includesexcluding a relationship among source data elements present in thesource of data.
 7. The computer program product of claim 6, whereinexcluding a relationship among source data elements present in thesource of data includes severing at least one of geometric and semanticrelationships among the source data elements.
 8. The computer programproduct of claim 6, wherein the relationship is imposed by a structuralconstruct of a source of a plurality of sources from which at least oneof the source data elements is sourced.
 9. The computer program productof claim 1, wherein the at least one constraint imposed by the sourcegeometry for the portion of the source data elements constrains adefinition of the portion of the data elements.
 10. The computer programproduct of claim 1, wherein the coercing facilitates content-emergent,inductive discovery through independent representation in the referencedata model of source content and structure of the source of data. 11.The computer program product of claim 1, wherein the coercing includesinduction-based content-centric feature mapping of at least one of thecontent data elements and the semantic data elements.
 12. A method ofblind semantic extraction and reconstitution comprising: accessing witha processor source data elements from a source of data stored in amemory, wherein content of the source data elements is defined by asource ontology and according to a source geometry; extracting thecontent for a portion of the source data elements as content dataelements; representing at least one source ontology definition for theportion of the source data elements, and at least one constraint imposedby the source geometry for the portion of the source data elements, assemantic data elements; and reconstituting a portion of the source dataelements in a reference data model in the memory by coercing a referencedata model ontology that is independent of the source ontology andindependent of the source geometry upon the content data elements andthe semantic data elements.
 13. The method of claim 12, wherein coercinga reference data model ontology includes informatic convolution ofelements of the reference data model ontology with the content dataelements and the semantic data elements.
 14. The method of claim 12,wherein constructing a reference data model includes representing atleast one geometry-derived constraint as one or more data elements inthe reference data model.
 15. The method of claim 12, wherein coercingfacilitates content-emergent discovery through independentrepresentation in the reference data model of source content andgeometry of the source of data.
 16. The method of claim 12, whereincoercing includes induction-based, content-centric feature mapping of atleast one of the content data elements and the semantic data elements.17. The method of claim 12, wherein coercing includes applyingtransformative mathematical operations upon the source data elementsthat maintain values of the source data elements while removingcontextual and relationship connections among the source data elementspresent in the source ontology.
 18. The method of claim 17, whereinapplying transformative mathematical operations causes redefinition of amathematical meaning of each source data element to which thetransformative mathematical operations are applied.
 19. The methodproduct of claim 12, wherein reconstituting includes severing logicalfunctionality of a source ontology from source data elements in thesource of data.
 20. The method of claim 12, wherein reconstitutingincludes severing connective, relational and correlative associationsamong source data elements.
 21. The method of claim 12, wherein thesource of data is characterized by composite schematic structures thatserve as discriminatory reference foci for semantical and lexical natureof the source data elements and wherein the semantical and lexicalnature of the source data elements is severed for reconstituting. 22.The method of claim 12, wherein the source ontology encompassesdefinitions that include meanings for the data elements, how the dataelements are connected, why the data elements are connected, how thedata elements are disposed in the source, why the data elements aredisposed in the source, alternate meanings for at least one of the dataelements and the structure of the source.
 23. The method of claim 12,wherein the source of data comprises source data elements drawn frommultiple disparate and disjoint domains.
 24. The method of claim 12,wherein the source of data is defined within dissimilar and disjointdomains.
 25. The method of claim 12, wherein the source data comprisesone or more of labeled and unlabeled information represented indisparate formats, structure and encodings.
 26. The method of claim 12,wherein reconstituting includes excluding a relationship among contentdata elements that is present in the source of data.
 27. The method ofclaim 12, wherein at least one source data element of the potion ofsource data elements is one or more of: non-decomposable, an aggregateassembled of a collection of non-decomposable items, a collection ofboth non-decomposable and decomposable items, and wherein reconstitutingincludes one or more of re-ordering the portion of source data elements,and re-assembling the portion of source data elements with a differentinformatic geometry.
 28. The method of claim 12, wherein reconstitutingincludes excluding a structure of the source of data.
 29. The method ofclaim 28, wherein the excluded structure includes one or more of astructure reposed within a relational database, a structure reposedwithin a tabular structure, a structure reposed within a formula, astructure reposed within a hierarchy, and a structure reposed withinunstructured and unlabeled collections of sources of the source data.30. A method of de-referencing comprising: accessing with a processorsource data elements from a plurality of sources of data stored in amemory, wherein each of the source data elements is defined by a sourceontology and according to a source geometry that corresponds to a sourceof the plurality of sources of data from which the each of the sourcedata elements is sourced; extracting a content portion of the sourcedata elements as content data elements; representing at least one sourceontology definition for each source of the plurality of sources of dataand at least one constraint imposed by the source geometry of the eachsource as semantic data elements; and producing a reference data modelin the memory by coercing a reference data model ontology upon thecontent data elements and the semantic data elements, wherein thereference data model ontology is independent of the source ontology andis independent of the source geometry.
 31. The method of claim 30,wherein producing a reference data model includes excluding a definitionof a source data element that is associated with a source of theplurality of sources of data from which the source data element issourced.
 32. The method of claim 31, wherein the excluded definition isdefined in an ontology for the source of the plurality of sources fromwhich the source data element is sourced.
 33. The method of claim 30,wherein producing a reference data model includes configuring one ormore of the content data elements and the semantic data elements in thereference data model independently of at least one semantic constraintpresent in a portion of the plurality of sources of data.
 34. The methodof claim 33, wherein the semantic constraint is imposed due to astructural construct of a corresponding source in the plurality ofsources of data.
 35. The method of claim 30, wherein producing areference data model includes representing in the reference data modelat least one constraint upon a content data element due to a structureof the source of the plurality of sources of data for a correspondingsource data element.
 36. The method of claim 30, wherein the coercingincludes coercing content data elements to comply with a target ontologythat is optimized for application-specific use of the reference datamodel.
 37. The method of claim 30, wherein coercing is performedindependent of a dependency imposed by the source geometry.
 38. Themethod of claim 30, wherein producing a reference data model includesdetermining an assertion model based on whether the coercing processesthe content data elements that represent a portion of the data elementsor the semantic data elements that represent at least one constraintimposed by the source geometry.
 39. The method of claim 30, whereinproducing a reference data model includes at least one of coercingre-combinations upon the content data items, coercing re-ordering uponthe content data items, and coercing re-labeling upon the content dataitems.
 40. The method of claim 30, wherein producing a reference datamodel includes severing interconnections defined in the source ontologyamong source data elements, the interconnections composed within atleast one of linked lists, tree structures, hierarchies, taxonomicstructures, and graph structures.
 41. The method of claim 30, whereinthe at least one constraint imposed by the source geometry comprises alabel outside of the plurality of sources of data.
 42. The method ofclaim 30, wherein the at least one constraint imposed by the sourcegeometry is derived from a hierarchy of a portion of the plurality ofsources of data.